By Charlotte Selvey A social evaluation of a community

 A social evaluation of a community-­‐based monitoring p roject in Guyana “cMRV: Community-­‐Measuring, Reporting and Verification” By Charlotte Selvey Supervised by: Prof. E.J. Milner-­‐Gulland & Mary Menton September 2013 A thesis submitted in partial fulfillment of the requirements of the degree of Master of Science and the Diploma of Imperial College London Declaration of Work I declare that this thesis, “A social evaluation of a community-­‐based monitoring project in Guyana. cMRV: Community-­‐Measuring, Reporting and Verification” is entirely my own work, and that where material could be construed as the work of others, it is fully cited and referenced, and/or with appropriate acknowledgement given. Signature Name of student Charlotte Selvey Name of Supervisor(s) E.J. Milner-­‐Gulland and Mary Menton ii Contents List of Figure …………………………………………………………………………………………….iii List of Tables……………………………………………………………………………………….……. v Abbreviations……………………………………………………………………………………………vii Abstract……………………………………………………………………………………………………viii Acknowledgements……………………………………………………………………………………ix 1. Introduction ………………………………………………………………………………………..1 1.2 Overall Aim…………………………………………………………………………………3 1.3 Research Objectives ……………………………………………………………………3 2. Background …………………………………………………………………………………………4 2.1 Evaluation and its role in Conservation ………………………………………. 4 2.2 Evaluation Types and Design ……………………………………………………. 5 2.2.1 Adaptive Management ………………………………………………… 5 2.2.2 Conceptual Model/Frameworks …………………………………… 6 2.2.3 Impact Assessments ……………………………………………………... 8 2.3 Integrating Conservation and Development …………………………………10 2.4 Using Social Network Analysis in Evaluations ……………………………. 11 2.5 REDD+ Explained ……………………………………………………………………… 12 2.6 Community Monitoring, Reporting and Verification ……………………. 14 2.7 Guyana’s Low Carbon Development Strategy ……………………………….15 2.8 Guyana and the North Rupununi …………………………………………………16 2.9 The cMRV Project ……………………………..……………………………..……...…. 17 2.9.1 Study Villages ……………………………..…………………………………19 3. Methods ……………………………..……………………………..………………………………… 22 3.1 Partners, Staff and Stakeholders of cMRV …………………………………… 22 3.2 Procedures ……………………………..…………………………………………………. 23 3.2.1 Planning phase and meetings …………………………………...…… 23 3.2.2 Village Procedures ……………………………..…………………………. 23 3.2.3 Pilot Activities ……………………………..……………………………….. 23 3.3 Project Independent Evaluation ……………………………..………………….. 24 3.3.1 GEF Guidelines ……………………………..………………………………. 24 3.3.2 Conceptual Model ……………………………..………………………….. 25 3.3.3 cMRV Objectives……………………………..…………………………….. 26 3.3.4 Participatory Workshops ……………………………..……………….. 26 3.3.5 The Problem Tree ……………………………..………………………….. 28 3.3.6 CREW and PMT Quiz ……………………………..……………………… 29 3.3.7 The Honesty Box ……………………………..……………………………. 29 3.4 Community Evaluation ……………………………..………………………………… 29 3.4.1 Village Interviews ……………………………..………………………….. 29 3.4.2 Project Attitudes ……………………………..…………………………….. 31 iii 3.5 Social Network Analysis ……………………………..……………………………….. 31 3.5.1 The Salience Score ……………………………..……………………….…. 31 3.5.2 Questionnaires with Partners ……………………………..……….… 32 3.5.3 Statistical Analysis ……………………………..…………………………. 33 4. Results ……………………………..……………………………..………………………………….….34 4.1 Impacts, Issues and Relevance of cMRV…………………………………………34 4.1.1 Village Perceptions of cMRV……………………………..…………….. 34 4.1.2 CREW and PMT evaluations ……………………………..……………..37 4.1.3 Key Issues of cMRV ……………………………..………………………….38 4.1.4 Perceptions of cMRV Objectives ……………………………..……….41 4.1.5 Local Relevance of cMRV ……………………………..………………….43 4.2 Capacity Built through cMRV……………………………..………………………….44 4.2.1 Villager Knowledge ……………………………..………………………….44 4.2.2 CREW’s Built Capacity ……………………………..………………………45 4.2.3 Technical Problems……………………………..………………………… 46 4.3 The ideal cMRV Champion ……………………………..……………………………. 48 4.3.1 Presence of CREW in Villages……………………………..…………… 48 4.3.2 Factors influencing information spread………………………...…..51 4.3.3 Factors effecting village attitudes………………………………….…..52 4.4 Social Sustainability of cMRV project……………………………..…………....….55 4.4.1 Individuals controlling information flow ……………………….….57 4.4.2 Individuals Influencing power within cMRV………………….…...58 4.4.3 Role of groups in spreading information within the cMRV….59 5. Discussion………………………..………………………………..………………………………....…63 5.1 Evaluating a Community-­‐based project ……………………………..………..….63 5.1.1 Adaptive Management in Community based conservation…64 5.2 Community Monitors and their presence ……………………………..………...64 5.2.1 Effective Communication Spread ……………………………..…….…66 5.2.2 Importance of strong leaders ………..……………………………..…...67 5.2.3 Building Capacity ……………………………..……………………………...67 5.3 Social Sustainability of cMRV……………………………..…………………………..68 5.4 Future research and recommendations……………………………..…………...69 5.5 Limitations ……………………………..………………………………..…………………...70 5.6 Concluding Remarks ……………………………..………………………………..……..70 References……………………………..………………………………..……………………………….…72 Appendices……………………………..………………………………..…………………………….…..80 iv List of Tables 3.1 ‘SMART’ project objectives for cMRV……..………………………………..……………..…26 3.2 Objectives, outputs and outcomes tested. .………………………………………………...27 4.1a Impacts caused by the cMRV project ………………………………………………….…. 35 4.1b Comments made by villagers………………………………………………………………….36 4.2 Results from environmental knowledge testing…………………………..……...….. 45 4.3 Results from CREW and PMT technical question of Quiz …………………..…….. 47 v List of Figures 2.1 Adaptive Management Cycle ……………………………..………………..…………………….6 2.2 Cambridge Conservation Forum’s Conceptual Framework …………………………7 2.3 Results Chain used during evaluations ……………………………………………..……...8 2.4 Map of North Rupununi and surrounding area……………………………………….…17 2.5 Map of Study village locations…………………………………………………………………..19 3.1 Criteria for evaluation developed……………………………………………………….……24 3.2 Conceptual model………………………………………………………………………...…………25 3.3 Problem Tree Diagram……………..…………………………………………………………..…28 3.4 Age structure of village interviewees……………………………………………………….29 4.2 Positive and Negative viewpoints held by villagers ………………………………... 37 4.3 Impact themes mentioned by CREW & PMT …………………………………………... 38 4.4 An example Problem Tree. ……………………………………………………………………... 39 4.5 Responses given from CREW&PMT……………………………..…………………………. 42 4.6 Number of villagers who have heard of cMRV and can explain cMRV………. 44 4.7a Apoteri Village Respect Salience……………………………..…………………………… 48 4.7b Fairview Village Communication Salience……………………………..……………... 49 4.7c Rupertee Village Communication Salience ……………………………..……………….49 4.7d Annai-­‐Central Village Respect Salience……………………………..……………………50 4.8 Occurrences of villagers who hold the highest cultural salience scores in their village……………………………..………..……………….……………..………………………………….. 50 4.9a Number of village respondents who understanding of cMRV in relation to distance and their village from BHI. ……………………………..………………………………. 51 4.9b Number of village respondents with good understanding of cMRV in relation to CREW cultural salience scores per village……………………………..………………………. 52 4.10a The effect of CREW salience within communities on village attitude towards the project. ……………………………..………..……………………………..……………………………53 4.10b Village distance from BHI correlated with village attitude towards ……….54 4.11 cMRV network plot using communication frequency as a proxy for connectedness within the network. Node numbers are unique codes of the 53 individuals involved in cMRV ……………………………..………..………………………….…….56 4.12 Box and Whisker Diagram showing degree and betweenness centrality partner and staff scores for communication frequency. ……………………………..….. …………..59 4.13 Box and Whisker Diagram showing degree and betweenness centrality partner and staff scores for communication reason……………………………..…………………...... 60 4.14 Box and Whisker Diagram showing degree and betweenness centrality for partner and staff scores for communication type..……………………………..…………... 62 Images © Charlotte Selvey, unless otherwise stated. vi Abbreviations cMRV Community Measuring, Reporting and Verification PMT Project Management Team CREW Community Resource Environmental Worker NRDDB North Rupununi District Development Board LCDS Low Carbon Development Strategy GCP Global Canopy Programme EJMG Prof. E.J. Milner-­‐Gulland (Primary Supervisor) BPF Ben Palmer Fry CS Charlotte Selvey BHI Bina Hill Institute SNA Social Network Analysis vii Abstract Community-­‐based monitoring of natural resources is essential for forest-­‐dependent communities to benefit from future REDD+ schemes, however monitoring is not a common practice in developing countries and has recently been targeted by conservation organisations in order to train local communities to undertake this ongoing process. This thesis has used a range of evaluation types to evaluate a community-­‐based monitoring scheme, the cMRV: Community Measuring, Reporting and Verification project, which helps local communities living in and around tropical forests in Guyana to more actively engage in future REDD+ schemes. The use of social network analysis in evaluations has been explored and findings conclude that communication and cooperation within projects, and between projects and local communities, is paramount for long-­‐term sustainability of community-­‐based monitoring schemes. Capacity building and education are necessary to support development of local expertise in order to encourage local management of natural resources. Word Count: 15,400 viii Acknowledgments Firstly I would like to thank my supervisors, Prof E.J. Milner-­‐Gulland (Imperial College London) and Mary Menton (Global Canopy Programme) for their support, guidance and encouragement through the planning stages, fieldwork and write up of this study. My fantastic experience in Guyana is thanks to all the Project Management Team, Community Resource Environmental Workers, North Rupununi District Development Board and Iwokrama for their kind hospitality and warm welcome to North Rupununi. Particular thanks goes to the CREWs and village councilors who guided us through their villages during our stays. A special thanks goes to Paulette and Daniel Allicock for making me feel part of the family during my stay in Surama, and of course Scott for giving up his bedroom during my stay. A thank you should also go to Ben Palmer Fry, Claudia Comberti for being perfect company in the field and providing encouragement and moral support. Back at Silwood I want to thank Billy Fairburn, Ana Nuno, and Leejiah Dorward. Without their help my results would unquestionably be lacking. For my sanity during long stays on campus: the ConSci’s 2013 who have all supported me considerably. Finally, without the financial support from both Imperial-­‐ for covering costs in the field and The Global Canopy Programme-­‐ for travel expenses, I would not have been able to undertake this study and for that I am extremely grateful. ix 1. Introduction Human pressures on natural resources are continuously rising in response to population growth and growing consumption habits. The inevitable outcome is that the more humans on Earth, the more destruction and degradation to natural resources habitats occurs (Pretty, 2003; Cincotta et al., 2000). Human population growth rate is uneven around the world, with substantially higher population growth occurring in biodiversity hotspots: highly threatened and highly endemic areas (Cincotta et al., 2000). Over 20% of world population live within hotspots, which cover just 12% of land on Earth (Cincotta et al., 2000). Increasing the coverage of protected areas have been an ongoing strategy to preserve irreplaceable ecosystems in the face of these rising human pressures (Wells & McShane, 2004). Tropical forests cover 15% of the worlds land surface (FAO, 2010; Parker et al., 2009), yet face a high rate of conversion to other land uses such as agriculture where 13-­‐
million hectares were lost each year over the last decade (FAO, 2010). Emissions from deforestation and degradation contribute between 15-­‐17% of the world’s greenhouse gases leading to global climate change (Sukhdevb et al., 2012). Communities dependent on resources sensitive to changes in climate lack the ability to adapt to climate variations and extremes (Adger et al., 2003; Smit et al., 2000). Efforts to reduce rates of deforestation and forest degradation in order to reduce global emissions have increased since 2007 (Thompson et al, 2011) as conserving tropical forests play a vital role in combatting climate change (Parker et al., 2009). Reduce Emissions from Deforestation and Degradation (REDD) is mechanism that allows countries with forests and stakeholders with forest rights to be financially compensated for their conservation efforts through carbon credit schemes (Scholz & Schmidt, 2008; Funder, 2009). REDD has the potential to: alleviate poverty; improve forest management, policies and practices; increase accountable governance mechanisms (Funder, 2009). Previous efforts to reduce global deforestation rates have proved unsuccessful but many believe “REDD provides a new framework to allow deforesting countries to break this historical trend” (Parker et al., 2009:18). 1 Within conservation science trends are now moving towards community-­‐based conservation due to the growing recognition that conservation success is not just determined by biological diversity; conservation problems are often caused by underlying socio-­‐economic and political forces (Wilder & Walpole, 2008). Monitoring of natural resources is essential to benefit from REDD+ schemes however monitoring is not common practice in developing countries due to smaller budgets and less skilled monitors (Danielsen et al., 2009). Community-­‐based monitoring is beneficial in these circumstances to advice decision-­‐making to better address key threats to natural resources and better management (Danielson et al., 2009; Danielson et al., 2005). If well designed, community-­‐based monitoring schemes can yield the same results as professional monitoring (Danielson et al., 2005), and take less time than scientist-­‐executed monitoring to influence village level resource management decisions (Danielsen et al., 2010). Achieving local cooperation without compromising conservation goals is becoming a new priority for the new generation of biodiversity conservation programmes (Wells & McShane, 2004). Conservation practitioners are increasingly recognising the benefits of incorporating socio-­‐economic goals as well as ecological ones by adopting co-­‐management and community-­‐based resource management: also known as community-­‐based conservation (Brooks, et al.,2012; Kleiman et al., 2000). This study evaluates a community-­‐based monitoring scheme, the cMRV which helps local communities living in and around tropical forests in Guyana to more actively engage in future REDD+ schemes. Understanding the effectiveness and impact of community-­‐based monitoring projects contributes to the conservation literature surrounding the value of local empowerment derived from successful community-­‐
based monitoring schemes (Danielson et al., 2005). 2 1.1 Overall Aim To assess the likelihood of the cMRV project’s long-­‐term sustainability by i) undertaking an independent evaluation of the project using partner, staff and village participation and ii) using Social Network Analysis (SNA) to identify key stakeholders integral to the longevity of the cMRV project. In doing so, this study not only provides insight into how social network analysis can be used in conservation evaluations but will also draw upon lessons learnt to ensure future cMRV projects in other areas of Guyana, Amazonia and beyond can be successful. 1.2 Research Objectives Objectives Research Questions 1. Undertake an independent evaluation a) What are the positive/negative impacts of of the cMRV project in Guyana using GEF the project? guidelines and a conceptual framework. 2. Assess the strength of relationships a) Do the CREW have a strong presence in the CREW have with the communities in villages? their villages and whether information on cMRV and its importance has been b) Identify local champions in each study effectively and appropriately reported to village to highlight to the cMRV project; the communities. 3. Using SNA, assess the likelihood of a) Identify key individuals and groups that long-­‐term sustainability of the cMRV have strong influences on decisions made project. within cMRV and analyse the strength of ties within the network; b) Identify key partners and individuals who are most important for influencing people’s opinions/ attitudes. 4. Make recommendations, which can be used within a toolkit framework, for __ future implementation of cMRV projects. 3 2. Background 2.1 Evaluation and its role in Conservation The quantity and quality of evaluations in conservation currently lags behind other sectors (Howe & Milner-­‐Gulland, 2012). The importance of rigorous evaluations has recently been highlighted by various organisations, including the Global Environment Facility and Centre for International Development, who explain that evaluations are a systematic assessment of what works, what doesn’t and why, which can be of directly useful to the implementing organisation (GEF, 2010; Pritchett et al, 2012). Monitoring and evaluation (M&E) is used within conservation projects to measure if interventions have been effective and ultimately lead to better decision making and better conservation in the long-­‐term (Stem et al., 2005). Although evaluations are vital for improving conservation program performance, both internal and external peer-­‐reviewed evaluations are seldom used in conservation due to lack of funding and often the fear of unwanted recommendations which could be difficult to apply (Kleiman et al., 2000). Nevertheless, the accountability and adaptability provided by evaluations in project implementation underpins their utility within the conservation arena (Margoluis et al., 2009b) Evaluations should aim to answer the question: “does the intervention work better than no intervention at all” (Ferraro & Pattanayak, 2006: 0482)? They are a measure of what is achieved compared to the project objectives; the aimed achievements (Margoluis et al., 2009a). Evaluations should not only monitor whether goals were reached, but try to understand the reasons for the successes or failures in reaching those conservation goals (Kleiman et al., 2000). The need for state-­‐of-­‐the-­‐art program evaluations has been advocated by many; notably the Millenium Ecosystem Assessment who highlight that even the most common biodiversity conservation programs should use well-­‐designed empirical assessments for evaluation (MEA, 2005). Indeed, the undertaking of rigorous evaluations have often been no more expensive or complicated than the conservation intervention itself (Ferraro & Pattanayak, 2006). 4 The best evaluations are inclusive of all stakeholders and participants, increasing the sense of local ownership of the project (Kleiman et al., 2000). Making the conservation project participatory throughout the project, especially in the planning phase, will ensure project goals are synchronised with local opinions and goals, making the evaluation effective (Kleiman et al., 2000). The fact that donors do not want to fund unsuccessful projects (Redford & Taber, 2000), creates a cycle of under-­‐reporting of unsuccessful results by conservation programs. This bias must be addressed, because an appreciation of successes and failures in adaptive management is fundamentally valuable to international conservation (Redford & Taber, 2000). A new culture where experimental learning is given as much praise as project success is needed (Redford & Taber, 2000). 2.2 Evaluation Types and Design Although the concept of evaluations has grown in conservation planning and management, the design of evaluations used is often incorrect for the project under evaluation when considering the array of possible options available (Margoluis et al., 2009b). Ferraro and Pattanayak (2006) provide a comprehensive review of the types of empirical evaluations available to conservation practicioners. 2.2.1 Adaptive Management Experimental learning or ‘adaptive management’ can be used to directly feedback into decision loops and Box 2.1 “Adaptation is fundamentally a way of incorporating reflection into project design (Pritchett et al., 2012) , to achieve the action to enhance the practice of best conservation and human wellbeing outcomes through systematically testing assumptions in order to adapt and learn (Wells & McShane, 2004; Salafsky et al., 2001). conservation and learning”. (Salafsky et al., 2001: 7) In practice, adaptive management is an experimental approach which produces reliable knowledge from experience (Stem et al., 2005). Responsibility lies at field level, where skilled managers use their judgement and resources (Wells & McShane, 2004) to respond to results found from the ‘analyse’ step (Figure 2.1). 5 Monitoring Plan: Assess the progress of the project and implement monitoring and management plans Management plan: actions required by team to gain the results wanted Analyse Data collected durng monitoring and communicate results Design a conceptual model: develop goals, stratgies, assumptions and objectives Iterate: use results to adapt and learn Start: Depine scope and establish a clear and common purpose (Mission) Figure 2.1 -­‐ Adaptive Management Cycle (adapted from: Salafsky et al., 2001) 2.2.2 Conceptual Models/ Frameworks Conceptual models can be used to compare pre and post-­‐ intervention, and provide a clear framework for managers to develop goals and objectives which can alter the threats to the project’s conservation targets (Margoluis et al., 2009a). Models are simplified versions of reality (Salafsky et al.,2001), thus a conceptual model outlines a set of relationships between certain factors that are believed to impact upon, or lead to, your conservation target (Margoluis & Salafsky, 1998). Conceptual frameworks are a visual representation (Figure 2.2) of cause and effect relationships in a logical order (Stem et al., 2005). The Cambridge Conservation Forum (CCF) has developed a range of conceptual frameworks supporting “systematic analysis of conservation effectiveness”, which are applicable to disparate conservation implementations; education and awareness projects, research and conservation planning, and training and capacity building 6 (Figure 2.2) (Kapos et al., 2009; 2008:155). Noteworthy is their scorecard, which grants conservation professionals a means of self-­‐evaluation in assessing conservation performance by measuring the implementation against the outcome (Kapos et al., 2009). This type of evaluation supports the recent shift in conservation interventions reporting on outcomes and effects rather than inputs, such as money and time spent and outputs, the countable products derived from activities completed (Kapos et al., 2008; Margoluis et al., 2009a) Figure 2.2 – Cambridge Conservation Forum’s Conceptual Framework for Capacity Building Efforts. The star highlights the key outcome of the framework (Source: Kapos et al., 2009) Alternatively, conservation implementations can be displayed via results chains, a form of conceptual model ubiquitous in planning and evaluation but provide less detail than CCF conceptual model. 7 Evaluations often concentrate on the inputs and outputs of a conservation project, especially as outcomes are often subtle and timely to manifest and thus under reported (Howe & Milner-­‐Gulland, 2012). During evaluations, in integrating between social sciences and biology, it is imperative to measure and highlight both the outcomes and impacts in terms of human knowledge, attitudes and behaviour, and ecosystem health (Margoluis et al., 2009a). Impacts and outcomes are the final parts of the results chain or logic model shown in Figure 2.3. Characterising the key outcomes provides a much more reliable proxy for whether a project will deliver real benefits and track the conservation impacts (Kapos et al., 2009). Inputs Strategies •  Resources eg. staff, time, money •  Activities undertaken by the project eg. training, monitoring Outputs Outcomes Impact •  Initial results from project activities eg. number of people trained •  Interim results or objectives achieved by outputs eg. attitude change, increased environmental awareness •  Desired end goals of project eg. target population increase, increased forest cover Figure 2.3 – Results Chain used during evaluations (adapted from Margoluis et al., 2009b) 2.2.3 Impact Assessments A one-­‐time impact assessment (IA) is frequently used to gauge project effectiveness, by investigating the positive and negative effects of the intervention (Stem et al., 2005) in terms of outcomes and impacts (Margoluis et al., 2009b; Rossi et al., 1999). 8 Social impacts are the consequences to humans due to private or public actions that alter local livelihoods or cultures (Richards & Panfil, 2011; IAIA, 2003).IAs have become increasingly popular with donor agencies where the goals of IAs look to prove the impacts and improve interventions (Hulme, 2000). It is now widely accepted that biodiversity conservation should have no negative impact on indigenous communities and should contribute towards poverty reduction (Schreckenberg et al., 2010; CBD, 2013). The Convention on Biologocal Diversity emphasises the importance of fair and equitable sharing of the benefits from protecting biodiversity (CBD, 2013), after a long history of responding to threats of habitat and species loss with strict nature reserves (Adams et al., 2004). This has become ever more relevant in recent years with protected areas expanding in developing countries under agreements to reduce emissions from deforestation and degredation (REDD) (Andam et al., 2010; Gullison et al., 2007). Protecting ecosystems through reserves has been argued to exaccerbate poverty due to limitions of both agricultural expansion and natural resource exploitation (Andam et al., 2008; Andam et al., 2010), displacing local people from their lands and resources (Schreckenberg et al., 2010). Conservation organisations have started including social objectives to assess social impacts resulting from interventions (Schreckenberg et al., 2010). IAs are often more highly demanded by sponsors than other types of evaluations as they improve the efficiency and effectiveness of the project activities (Hulme, 2000). The difference between the outcome in absence of an intervention (the counterfactual) and the outcome with an intervention is the impact, be it positive or negative (Caplow et al., 2011; Hulme, 2000). However there is no standard method for assessing those impacts (Schreckenberg et al., 2010) Each conservation project has different trade-­‐offs for choosing a specific type of evaluation. There is no ‘one-­‐size-­‐fits-­‐all’ model for evaluation and the trade-­‐off between project factors (time, funding, expertise) will define what type of evaluation best suits (Margoluis et al., 2009a). Routinely, monitoring outcomes can be time-­‐
consuming and costly (Howe & Milner-­‐Gulland, 2012), so less intensive evaluations that are still systematic and allow continuous adaptive management are favoured (Stem et al., 2005); practitioners can constantly tweak their implemented actions to 9 modify decisions for best practice (Margoluis et al., 2009a). Although quantitative surveys are common in impact evaluations, a wealth of valuable information regarding anecdotal impacts and changes to people’s lives is lost unless qualitative assessments are undertaken (Wilder & Walpole, 2008). Measuring the most significant impact in people’s lives over a period of time works not only as a qualitative monitoring tool, but as a participatory management tool to assess staff capacity (Wilder & Walpole, 2008). 2.3 Integrating Conservation and Development Integrated Conservation and Development Projects (ICDPs) aim to develop sustainable management of natural resources whilst fulfilling the socio-­‐economic needs of the stakeholders at local, national and international levels (Wells & McShane, 2004; Mistry et al., 2010). ICDPs marry site based conservation with social and economic development goals and employ community-­‐based conservation to incorporate the goals of both conservation and development (Wells & McShane, 2004; Wells & Brandon, 1992), involving communities as active stakeholders (Brooks, et al., 2012). Local cooperation during conservation interventions is pivotal if conservation goals are to be sustainable and effective long-­‐term (Wells & McShane, 2004;Berkes, 2004), as is the linking of conservation actions over organisational levels when multiple stakeholders are involved. This engenders a bottom-­‐up process where conservation actions, solutions and goals are developed from the lowest possible organisational levels (Berkes, 2004) More recently ICDPs have integrated adaptive management to combat the failings in previous ICDPs due to factors such as weak assumptions, overly optimistic goal-­‐
setting, and unconvincing local participation (Mistry et al., 2010; Wells & McShane, 2004). The wellbeing of local people and the environment have been found to be complementary interdependent on each other, creating socio-­‐ecological systems, with human wellbeing inextricably tied to the state of ecosystems in which they live (Brown & Kasser, 2005; Coulthard et al., 2011; Mistry et al., 2011). Using a social wellbeing approach to conservation offers a holistic view of the societal impacts 10 encountered from policies implemented and governance regimes (Coulthard et al., 2011). Moreover, recognising that social and ecological landscapes are interconnected aids researchers analysing the interplay of conservation and development (Mistry et al., 2010). Within community-­‐based conservation and ICDPs the use of both local and external knowledge, using a multi-­‐stakeholder perspective and active participation, allows for better adaptive management (Mistry et al., 2010;Sayer & Campbell, 2004). A move away from expert-­‐led approaches of natural resource management into local participation and co-­‐management encourages stakeholders to be involves at all levels (Mistry et al., 2011). Strengthening local capacity in order to practice adaptive management and take the lead in finding long-­‐term solutions to sustainably manage their resources (Mistry et al.,2011). 2.4 Using Social Network Analysis in Evaluations Social anthropologist Radcliffe-­‐Brown first developed the idea of social networks in the earlier 1930’s before a wave of social network specialists began devising methods for translating social network descriptions (Degenne & Forse, 1999; Scott, 2013). Over the last decade, an increased research effort around social network analysis (SNA), across the social and physical sciences, has galvanised the understanding of how individuals combine to form a web of interactions and relations which can affect a goal of an organisation (Borgatti et al., 2009; Borgatti & Foster, 2003). One discipline gathering interest in SNA is adaptive natural resource management and governance, especially participation and co-­‐management based schemes (Bodin et al., 2006). Analysing networks in this area is a welcomed new path due to the decline and degradation of natural resources despite increased conservation efforts (Bodin & Crona, 2009). Analysing stakeholder characteristics within a network helps extricate the underlying reasons which either enhance or hinder natural resource governance and conservation initiatives (Freeman, 2004). 11 Social networks can be more important than formal institutions when concerned with compliance to environmental regulations (Bodin & Crona, 2009; Scholz & Wang, 2006). Furthermore the patterns of social network structures often affect the rate of information exchange as well as the flow of ideas and resources (Belaire et al, 2011). Given the diverse stakeholder interests that envelop conservation and natural resource management, studying social network structure, although complicated, is necessary to encourage collaboration between different actors and aid management coordination (Bodin, et al., 2006; Belaire et al, 2011). Interactions between stakeholders helps build capacity, envourage knowledge sharing and social capita as well as developing trust within networks (Belaire et al, 2011; Borgatti & Foster, 2003; Pretty, 2003). Encouraging social capacity lowers the transaction costs of working together and enables facilitation between stakeholders (Pretty, 2003). Other areas important for a collaborative network are listed in Box 2.2. Box 2.2 -­‐ Four features necessary for a collaborative network: 1. Relations of trust; 2. Reciprocity and exchanges; 3. Common rules, norms and sanctions; 4. Connectedness in networks and groups. (Pretty, 2003: 1913) Finally, SNA can be used to help identify ideal stakeholders through communication links between individuals in a network rather than subjectively choosing stakeholders on their power and influence abilities (Prell et al., 2011). 2.5 REDD+ explained The first explorations of mechanisms for REDD were discussed in 2005 after the UNFCCC Conference of Parties (COP) 11 in Montreal (Parker et al., 2009). Implementing REDD would ultimately lead to decreasing the amount of carbon in the atmosphere released from deforestation and degradation. In Bali, Indonesia in 2007, COP 13 was held and the parties agreed through the Bali Action Plan to enhance “national/international action on mitigation of climate change (…)” through using “(…) positive incentives on issues relating to reducing emissions from deforestation and forest degradation in developing countries; and the role of 12 conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries” (UNFCCC, 2008:3). This addition of the conservation, enhancement of forests carbon stocks and sustainable management of forests is the ‘+’ in REDD+ and signifies carbon sequestration1 (Parker et al., 2009). This broader focus of REDD+ has attracted numerous organisations including the World Bank and UN organisations: the Food and Agriculture Organisation (FAO), the Unitied Nations Environment Program (UNEP) and the United Nations Development Program (UNDP), collectively known since 2008 as the UN-­‐REDD programme which supports national REDD+ readiness efforts in partner countries in Africa, Asia-­‐Pacific and Latin America (Thompson et al., 2009). Although the REDD+ framework gives developing countries an opportunity to financially benefit from protecting their forest natural resources, recent debates question the effectiveness of REDD+ schemes and the trade-­‐offs local communities may face. Labelling REDD+ mechanism as a ‘win-­‐win’ situation, as with any other conservation interventions, is ill-­‐advised. Instead the costs, hard choices and losses should be openly discussed and negotiated so not lead stakeholders towards unrealistic expectations (McShane et al., 2011). One major cost of REDD+ is the burden to the small-­‐scale agricultural sector. REDD+ schemes promote reforestation, which in turn increases land and food prices as agricultural expansion is halted (Funder, 2009). This causes concern as food insecurity is already a pressing issue (Pirard & Treyer, 2010). Funder (2009) reviews positive and negative impacts of REDD+ in detail. Although there is not yet international compliance for REDD+, a number of developed and developing countries have invested resources in negotiating multilateral and bilateral agreements to support REDD+ readiness (Sukhdevb et al., 2012). Pilot projects have been selling carbon credits through voluntary sectors such as the Verified Carbon Standard and Climate, Community and Biodiversity Standards to sub-­‐
national and private sector levels (Sukhdevb et al., 2012). 1 Carbon sequestration is the removal of carbon from the atmosphere by reforestation and forest enhancement (Parker et al., 2009). 13 Forest resources directly contribute to the livelihoods and wellbeing of 90% of the world’s 1.2 billion people living in extreme poverty (The World Bank, 2004). This necessitates that these resources are monitored for those who depend upon them (Palmer Fry, 2011), regardless of whether the communities benefit from external funding. The community is an essential stakeholder to engage with during REDD+ projects, where community-­‐based monitoring, reporting and verification becomes an increasingly relevant strategy which involves community participation to build a technically capable local workforce ready for REDD+ schemes in-­‐country. 2.6 Community Monitoring, Reporting and Verification (MRV) REDD+ has the potential to preserve and sustainably use the worlds remaining tropical forests, but in order for REDD+ to become legitimized/compulsory, a framework of transparent, accountable, and sustainable monitoring, reporting and verification (MRV) system is essential to develop to accurately quantify any emissions reductions from REDD+ activities (Palmer Fry, 2011; Fordham et al., 2011; Graham & Thorpe, 2009). Building capacity with communities in-­‐country permits locally-­‐based undertaking of MRV in REDD+ projects which reduces the cost of REDD, making it more sustainable, engages communities and generates a direct income through employment of local people whilst improving the equity and governance of REDD+ projects (Graham & Thorpe, 2009; Fordham et al., 2011; Danielsen et al., 2005). Community-­‐based monitoring has also been shown to i) increase social capital and capacity ii) build environmental awareness and education about sustainable natural resource management iii) strengthen relations between institutions and help build social autonomy, which can facilitate community-­‐based decision-­‐making (Evans & Guariguata, 2008; Danielsen et al., 2005). By empowering local people to take ownership of REDD+ through monitoring, undertaking forest inventory work, and collaboration between local stakeholders, fosters trust between different groups, ultimately leading to transparent, accountable and democratic decision-­‐making in national REDD programmes (Danielsen et al., 2005; Skutsch et al., 2009). Uptake of community-­‐based monitoring projects is rising internationally. One major initiative ran from 2003-­‐2009, the Kyoto: Think Global, Act Local programme (K: TGAL), a collaborative research project with partners based in Nepal, India, Tanzania, 14 Mali, Senegal, Guinea Bissau and Papua New Guinea (Skutsch, 2011). Results from K: TGAL suggest communities can be trained in forest-­‐carbon recording relatively easily, and at between 30-­‐50% of the cost of employing forest-­‐carbon professionals (Skutch, 2011; Skutsch & Ba, 2010; GCP, n.d). 2.7 Guyana’s The Low Carbon Development Strategy In June 2009, the former president of Guyana developed a national strategy to drive Guyana towards a low carbon economy and, with the help of negotiations from the United Nations and climate change experts, established the national Low Carbon Development Strategy (LCDS) (Office of the President, 2013). The main goals of the LCDS are set out in Box 2.3. Box 2.3 – Goals of the LCDS, Guyana 1. Transform Guyana’s economy to deliver greater economic and social development for the people of Guyana 2. Provide a model of how climate change can be addressed through low carbon development in developing countries for when international REDD+ schemes take affect. 3. Create a low-­‐deforestation, low carbon, climate-­‐resilient economy while combating climate change mainly through incentives to avoid deforestation. (Office of the President, 2013) The partnership between Norway and Guyana was made official after President Jagdeo of Guyana and Norway’s Minister of the Environment and International Development, signed a ‘Memorandum of Understanding’ in 2009, which bound Norway to provide Guyana with “result-­‐based payments for climate services of upto US$250 million by 2015”, in order to combat climate change (Office of the President, 2013:8). The LCDS has gained international recognition and was presented as a working example of a developing contry making significant progress to climate change at the RIO+20 United Nations International Development Conference, Rio De Jenero, Brazil (Office of Climate Change, 2012). 15 A separate issue concerns whether the local communities will benefit from payments for forest services or whether financial payments are too simplistic or even counterproductive, especially if only local elites seize the benefits (Berkes, 2004; Brown, 2002). 2.8 Guyana and The North Rupununi Guyana is the third smallest country in South America, with just under 740,000 people living within an area slightly smaller than the UK (Mistry et al., 2010; CIA, 2013). Due to a block from foreign investments until the 1990’s, Guyana has a high biological diversity and is one of the world’s ‘major tropical wilderness areas’ as defined by Russell Mittermeier (Mistry et al., 2004; Mittermeier, 1988; Cincotta et al., 2000). The Guyana Shield is one of the most pristine terrestrial ecoregions on Earth (Cincotta et al., 2000). Since the 1990’s, encouraged foreign exchange led to intensification of natural resource extraction, mainly logging and mining (Mistry et al., 2004; Mistry et al., 2010). Of Guyana’s 14 million hectares of forest, 8.8 million hectares is state owned, of which 8.2 million hectares has already been sold as logging concessions (Mistry et al., 2004; Richardson & Funk, 1999; Parry & Eden, 1997). However, only one third of the forest concession has been logged and the Guyana Shield boasts one of the lowest deforestation rates in the world (Parry & Eden, 1997; Richardson & Funk, 1999; Armenteras et al., 2009). The state owned forest now encroaches towards the North Rupununi savannahs and tropical forests in the south-­‐west of Guyana, close to the Brazilian border: Annai-­‐
District (Figure 2.4) (Richardson & Funk, 1999; Mistry et al., 2004; Mistry et al., 2010). Land tenure issues have become a major problem in the area as few indigenous communities have rights for the land they have subsisted from for centuries, becoming vulnerable to state licensed resource extraction (Mistry et al., 2010). The indigenous community consists of Makushi Amerindians who have a complex relationship with their environment where rituals practices are directly linked to natural resource use (Forte, 1996; Mistry et al., 2004). The threats to the North Rupununi ecosystems and the Makushi culture and livelihoods from logging and 16 mining have escalated in correlation with foreign investment interests in the area (Mistry et al., 2004). Significant funding to build local capacity for effective natural resource use in the region has been received from a variety of British and Guyanese institutions. Figure 2.4 – Map of North Rupununi and surrounding area. (Adapted from Berardi et al., 2013) 2.9 The cMRV Project The cMRV project is a collaboration between Global Canopy Programme and local Guyanese partners: Iwokrama International Centre for Rain Forest Conservation and Development and NRDDB, and was funded by the Norwegian Government’s development agency (GCP, 2013b). The project aimed to pilot a participatory and community-­‐based forest monitoring scheme with a view to feed into the national MRV plans: the LCDS (GCP, 2013). 17 The first phase of the project took place in the North Rupununi between June 2011 and June 2013. Local people were trained in measuring the health of their forests and their community’s wellbeing: a safeguard measurement to assess impacts of mitigation efforts on natural resources and community welfare (GCP, 2013b; UNFCCC, 2011). The trained community members are known as CREW: Community Resource Environmental Workers who collect data using open-­‐source software for handhelds. The data collected included: information on forest and savannah ecosystems, carbon storage, community wellbeing, and locally important resources, empowering communities to sustainably manage their resources and make informed decisions on whether to participate in REDD+ schemes (GCP, 2013b). This data, coupled with satellite-­‐derived remote sensing date allows for a robust and cost-­‐effective MRV system keeping in accordance with the UNFCCC guidelines (GCP, 2013b; UNFCCC, 2011). The second phase of the project is tasked with building on the lessons learnt from the first phase, to replicate the community monitoring in other regions of Amazonia and develop a cMRV best practices, supporting information exchange and inter-­‐project communication of community based-­‐monitoring projects (GCP, 2013a; GCP, 2013b). 18 2.9.1 Study Villages The North Rupununi District consists of 16 villages. The seven study villages are: Rupertee, Kwatamang, Annai-­‐Central, Surama Fair View, Apoteri and Katoka, (Figure 2.5). Time constraints dictated these choices; there was a focused effort to visit the furthest villages from the project centre at Bina Hill Institute (BHI) as visits to nearby communities was favoured in the past.. BHI is a centre that facilitates local development initiatives and a facility used for training where local projects are based from, including the cMRV project (V. Moses, PersComms2). Figure 2.5 – Map of Study village locations (Source: NRDDB, 2013) 2 Vivian (Ricky) Moses works as the GIS Specialist on the Project Management Team for cMRV-­‐Guyana project. 19 Kwatamang Situated at the heart of the North Rupununi District, Kwatamang hosts the NRDDB at BHI in Kwatamang. The village has 75 households and is a 15-­‐20 minute bike road to the Brazil-­‐Georgetown road. Kwatamang has greatest access to information about international issues and development/conservation programmes ongoing in the area. Rupertee A savannah community located next to the Brazil-­‐Georgetown road. Rupertee has 67 households and is situated close to BHI. Rupertee is recognized as a well-­‐connected village, highly represented at the North Rupununi District level and has exposure to external issues (Berardi et al., 2013). Annai-­‐Central (Annai) Annai has the most densely populated village with no farmland or river access and 78 households. Annai is part of Annai-­‐District consisting of five villages which share resources and farmlands: Surama, Annai, Wowetta, Kwatamang and Aranaputa. Annai itself owns a well-­‐used airstrip and is home to a respectable eco-­‐lodge, ‘Rock View’ which draws in international and national eco-­‐tourists. Surama This forest village is situated at the savannah-­‐forest boundary, 6km from the Georgetown-­‐Brazil road. The village has expanded in recent years and now holds 56 households. Surama Eco-­‐lodge is a community-­‐run eco-­‐tourism centre which brings tourism and income into the area. Katoka This river village consists of 128 households, spread between the main village of Katoka and its satellite village Simoni. From Yupukuari there is a 30-­‐minute car journey to the Brazil-­‐Georgetown road. Fairview A forest community lying within Iwokrama Forest, a protected area situated next to the Georgetown to Letham road and the Essiquibo River. The community has 55 20 households and has a strong relationship with Iwokrama. Although Fairview is physically furthest from BHI, they are linked by the main road. Apoteri Apoteri is comfortably the most remote village. It lies on the tributary where the Rupununi River meets the Essiquibo. From the main road it takes 30 minute by car followed by 3-­‐hour by boat journey to reach the village. 52 households make up the village. 21 3. Methods The methodological framework used throughout this study employed a variety of qualitative, quantitative and participatory methods. Participatory workshops were formulated using Robert Chambers (2002) as a guide to best practice. Techniques for conducting interviews and questionnaires were guided by substantial insights provided by Newing et al. (2011) and Bernard (2011). 3.1 Partners, staff and stakeholders of cMRV Project partners involved in developing the cMRV project: 1. North Rupununi District Development Board (NRDDB) – a local Amerindian Non-­‐Governmental Organisation (NGO) 2. Iwokrama International Centre for Rainforest Conservation and Development-­‐ National level NGO with long term community engagement in the area (Berardi et al., 2013) 3. The Global Canopy Programme (GCP) – a tropical forest think tank based in Oxford who oversee the logistics and funding of the cMRV project. Project staff, either full time employees or part-­‐time, of the cMRV project: 1. Project Management Team (PMT) of the cMRV – full time employees who oversee work completed by the CREW and form the ground based team who manages the cMRV project. 2. Community Resource Environmental Workers (CREWs) – part-­‐time employees and community and forest monitors of the cMRV project. Other stakeholders involved in the evaluation and decisions of the cMRV: 1. Village leaders (Toshoas) 2. Communities of the North Rupununi Region. 22 3.2 Procedures 3.2.1 Planning phase and meetings Several meetings were held with GCP and the cMRV project advisor (BPF). Discussions centred upon exercises to be included in workshops, stakeholders to be included and what outcomes the workshops were expected. It was decided that there would be two workshops: the first a two-­‐day workshop with just the CREW and PMT to ascertain lessons learnt and any concerns from the first phase of the cMRV project, the second a CREW-­‐only workshop. In-­‐country meetings were held with the PMT to discuss reasons for evaluation and social network analysis (SNA) and logistical arrangements were set for village visits. 3.2.2 Village Procedures Letters were drafted, signed and sent by the cMRV Project Coordinator (Appendix 5) to the seven study villages to prepare the community council and leaders for research being undertaking in their villages. This was to highlight the objectives and motivations of this study to encourage a mutual understand during interviews (Milner-­‐Gulland & Rowcliffe, 2007). 3.2.3 Pilot Activities GCP advisor (BPF) and Imperial College supervisor (EJMG) previewed all questionnaires and interviews. Amendments to question wording and layout were made before piloting. Social network questionnaires were piloted on friends and family; no amendments were needed. Interviews with villagers were piloted with 5 villagers in Rupertee. No amendments were made to the interview layout and questions but interview procedure was improved. 23 3.3 Project Independent Evaluation 3.3.1 GEF Guidelines Global Environment Facility’s monitoring and evaluation guidelines highlight five criteria are reviewed (Figure 3.1) to assess levels of achievement of project outcomes and objectives. (GEF, 2008). Figure 3.1 -­‐ Criteria for evaluation developed by CS (adapted from GEF, 2008; 2010). Stars indicate which areas have been evaluated under this study. Within sustainability, financial and environmental were not included and within results, global environmental benefits were not including in the scope of this evaluation. 24 3.3.2 Conceptual Model A conceptual model, developed by CS (Figure 3.2) was used to i) determine which objectives were achieved from GCP’s project objectives (Table 3.1) and ii) identify the key issues that needed to be considered as part of the evaluation. The model shows that the conservation impact feeds in to the overall goal of GCP. Figure 3.2 – Conceptual model adapted by CS from Kapos et al. (2009), framed around the results chain modified from GCP’s original objectives. The conceptual model is shown by the clear boxes, and the results chain are coloured. Orange circles are the areas, which could be tested during fieldwork and specific questions were asked regarding these. The star indicates the key outcome from the project, which, if proved correct, will lead to the conservation impact. 25 3.3.3 cMRV Objectives Measurable or ‘SMART’ objectives in natural resource management are important to develop. All objectives must possess five properties: (1) Specific, (2) Measurable, (3) Achievable, (4) Results-­‐oriented, and (5) Time-­‐fixed (Schroeder, 2009). In order to test objects in a conceptual framework it is necessary they are SMART. cMRV Objectives were modified by CS (Table 3.1). Table 3.1 – ‘SMART’ project objectives for cMRV Project “SMART” Criteria Objectives Objective 1 2 3 4 5 By project end, use an internet-­‐based platform to share information with donors about collected forest monitoring data. Train 32 community members in GPS handhelds and Information Technology for recording and sharing information by end of project. Use data collected from community-­‐based monitoring of forest carbon (and biodiversity, water and wellbeing) to inform the national MRV (LCDS) system by 2013. By July 2013, develop a long-­‐term monitoring methodology to measure the impacts of REDD+ on the forest and forest-­‐dependent communities. By end of project, develop a toolkit for other community MRVs to utilise in order to replicate the cMRV project and benefit from lessons learnt 3.3.4 Participatory Workshops With reference to Chambers' (2002),a range of exercises were planned and completed during both workshops. Table 3.2 highlights the types of group exercises and surveys, the objectives they address and the stakeholders involved. 26 Table 3.2 Objectives, outputs and outcomes tested. Exercise/ Survey Quiz Objectives addressed Smart Objective 1: Use an internet-­‐based platform to share information with donors about collected forest monitoring data. Smart Objective 2: Train 32 community members in GPS handhelds and Information Technology for recording and sharing information. Smart Objective 3: Use data collected from community-­‐based monitoring of forest carbon (and biodiversity, water and wellbeing) to inform the national MRV (LCDS) system. Smart Objective 4 and Output: Develop a long-­‐term monitoring methodology to measure the impacts of REDD+ on the forest and forest-­‐dependent communities. Smart Objective 5 and Output: Develop toolkit for other community MRVs to utilise Stakeholders involved CREW and PMT Honesty Box Smart Objective 2 and Output: 32 community members trained (figure 3.2). CREW Problem Tree Project Evaluation – GEF “Results” (figure 3.1) CREW Village Interviews Outcome: “Data obtained is freely available to the local community, government, donors and other stakeholders” Local Villagers Outcome: “Community forest monitors are employed and actively engaged in regular reliable (audited) monitoring activities” Social Network Social sustainability of project (figure 3.1) Questionnaire Smart Objective 4 and Output: By July 2013, develop a long-­‐term monitoring methodology to measure the impacts of REDD+ on the forest and forest-­‐dependent communities. GCP, Iwokrama, CREW, NRDDB and PMT 27 3.3.5 Problem Tree The ‘problem tree’ exercise was chosen as participants can visualise the relationship between the causes and effects of project problems, also known as a situational analysis (Figure 3.3) (Gómez & Pacha, 2013). Partitioning a problem into its components allows the participant to discuss and clearly prioritise what is most important to focus on during an evaluation (Gómez & Pacha, 2013). Figure 3.3 -­‐ Problem Tree Diagram (developed by CS) 28 3.3.6 CREW and PMT Quiz A short quiz was handed out during the CREW-­‐only run by CS and BPF (Appendix 2). The quiz was anonymously completed to encourage truthful answers, especially to the open ended questions about project impact. 3.3.7 The Honesty Box An exercise was conducted where CREWs could anonymously write on cards whether they had received enough training to do their jobs well. The cards were then placed in the ‘honesty box’ in their own time during the workshop. Anonymity encouraged CREWs to be truthful about their answers and removed response bias that may have existed with GCP and PMT present. 3.4 Community Evaluations 3.4.1 Village Interviews Ten interviews per village were selected to assure sufficient statistical power. Systematic sampling was straightforward to achieve ten interviews per village whilst remaining representative (Milner-­‐Gulland & Rowcliffe, 2007). The age structure of total village interviewees (Figure 3.4). Those under 18 years old were at school during interviewing hours thus not represented in the sample. 70+"
50,69"
Age$Range$
30,49"
18,29"
Under"18"
0"
5"
10"
15"
20"
25"
No.$of$interviewees$within$each$age$bracket$
$
Figure 3.4 – Age structure of village interviewees A gender stratified systematic sample of the population in each village was chosen and adapted from McDowell (2011). Villages in the North Rupununi lack standardised house numbering systems, making it difficult to randomly select households. By dividing the total households (H) per village by 10 gives the sample household 29 interval (s), to count in-­‐between each surveyed household and rounded down to nearest whole number. In case no household member was present, the closest neighbour was chosen. Counting in between sampled houses was undertaken in a contextually appropriate and practical manner. s=H/10 Recall was used during the interviews where individuals expressed changes that occurred over the past year due to cMRV. This recall period is a relevant time period to grasp the perceived impact of the cMRV project to people’s lives (Milner-­‐Gulland & Rowcliffe, 2007). Villagers were also interviewed to gauge their opinions towards the project and if any positive or negative impacts had been felt in the communities so far. Open-­‐ended questions were used for anecdotal evidence of change, often missed from purely quantitative monitoring (Wilder & Waldpole, 2008). The Most Significant Change method highlighted by Wilder and Waldpole (2008) allows the interviewee to describe their feelings about the project and give suggestions of how to improve the project. Positive and negative themes were extracted from questions 17 and 18 on the questionnaire (Appendix 3) and grouped into categories (Tables 4.1a,b) which have values attached to them. The questions asked were: Question 17: “In your opinion, thinking back over the past year, has anything changed significantly for you or anyone around you as a result of the cMRV project?” Question 18: “Please write down any comments you want to make about the cMRV project.” 30 3.4.2 Project Attitudes Individual viewpoints of the project were subjectively collated into a positive, negative or ambivalent viewpoint based upon the ‘impact of the project’ responses and comments made by each interviewee. A general overall feel for individuals’ views are defined as their attitude. Overall village attitudes were also given a subjective score from responses of ten interviewees per village. Village attitude scores are based upon the feel from total responses. If several negative and positive comments were made, a subjective overall attitude was given based upon the type/severity of comment made. 3.5 Social Network Analysis 3.5.1 The Salience Score As part of village interviews a social network aspect was included to identify key and influential villagers to the village. These key members of each community were calculated using a salience score, developed by Mitchell (1997) and modified by Papworth et al., (2013), of the names mentioned by each interviewee. These key members have been classed as potential project “champions” which are peer regarded and hold attributes essential for promoting collective action among peers needed for implementing effective community-­‐based conservation (Knight et al., 2010). Interviewees were asked to name the top 5 people they speak to, trust and respect the most. Salience calculations were made using S. Papworth’s et al.’s (2013) assumption: items named by i) more individuals and ii) earlier on lists are more salient. The following equation was used to calculate the salience of each village member (Papworth et al., 2013; Quinlan, 2005): 31 Length is the number of names given by individual (i), and position is the location of a specific person in the list of that individual. If an individual is not mentioned by an interviewee their salience is zero (Papworth et al., 2013). The cultural salience of each village member is calculated using the following equation, where ‘n’ is the number of individuals interviewed: Some analyses also note combined cultural salience scores. This is the sum of both CREW members’ cultural salience score in all their salience of: trust, respect and communication. 3.5.2 Questionnaires with Partners A social network questionnaire was dispatched by email or in person to all project partners and staff of the cMRV project (Appendix 4). Respondents were asked who in the cMRV project they communicated with most frequently, what they communicated about and which partners/staff they trusted the most. These questions form the basis of the social network analysis. In order to analyse social networks a number of centrality measures were calculated: betweenness and degree, the most popular measures (Brandes, 2008). These measures summarise a node’s (person’s) contribution to the cohesiveness of a network (S. P. Borgatti & Everett, 2005). Density of networks are important to measure in organisations that depend on coordination of activities (Hawe et al., 2004). Dense networks indicate that everyone knows everyone and the maximum possible links between nodes have been made (Degenne & Forse, 1999). A higher density implies there is more potential for communication, collaboration and exchange of ideas across the entire network (Belaire et al., 2011; Bodin & Crona, 2009). However a negative of this occurs when one actor wishes to deviate from the norm of their social group (Hawe et al., 2004). 32 Here, loose-­‐knit networks are better suited for interventions, which lead to a deviation from cultural norms (Bott, 1957). Betweenness classifies where a node lies between the shortest communication paths of other node pairs (Brandes, 2008; Freeman, 1977). It can be inferred from this type of centrality score the potential power an actor has in distorting or slowing down communication flows between the pair of nodes it sits on (Borgatti, et al., 2009) Degree centrality is a measure of an individual’s or organisation’s popularity or activity as measured by the number of social connections (ties) to others (Belaire et al., 2011;Degenne&Forse, 1999). A high degree centrality score means a person is more central to the network and relies upon fewer people as bridges of communication transactions throughout the network (Degenne&Forse, 1999). 3.5.3 Statistical Analysis All analyses were conducted in Microsoft Excel or R (Version 2.14.0) (R Core-­‐Team, 2013). A variety of centrality scores, including betweenness centrality and degree centrality were calculated within R package: ‘Igraph’. Qualitative analysis included open-­‐ended questions, which were coded and put into common themes in order to interpret in textual analysis (Newing et al., 2011). 33 4. Results 4.1 Impacts, Issues and Relevance of cMRV 4.1.1 Village Perceptions of cMRV During village interviews several questions were asked to those who had heard of the cMRV project in order to gauge impacts communities felt, if any. Question 17: “In your opinion, thinking back over the past year, has anything changed significantly for you or anyone around you as a result of the cMRV project?” Thirty-­‐seven people stated either a negative or positive impact from the project (Table 4.1a). The most prominent and repeated project impact is environmental awareness. The overall feeling from the seven study villages was a positive (32 positive and 5 negative statements). 34 Table 4.1a –Impacts caused by the cMRV project, mentioned by respondents who had heard of cMRV. Impact Themes Environmental Awareness Training Communities Resource Availability Knowing what’s around Example Quotes “People are getting to know about their resources.” “MRV has made me more aware of the changes in climate.” “There are now well trained people (in high tech) who can be ready for national MRV.” “We're getting advice from them (MRV). It’s changed the behaviour of people in the community. People not cutting so much.” “You cant cut forest all the time, like you can't waste what you have got.” “People wait longer than a year to move to a new farm now.” “We know what’s at the back of the mountain now.” Frequency mentioned 13 9 8 1 Tourism “People come and visit here more (like us, GCP). More white people come and more buildings are going up.” 1 Unequal Knowledge Sharing Community Changes “(…) only some people are being taught about MRV.” “They never tell me about it.” “Community has changed. A bad change. We have less jobs in our community now.” 3 Land Use Conflicts “(…) because of the mapping people are saying you can’t come and fish in our river or farm on our land now, we have been fishing and farming there for years and they only say this now.” Ambivalent/ Not “People don’t want to do farming anymore.” direct impact “People now check farms twice a week because bush deer have from cMRV. increased and disturb farms.” “The new Toshoa [village chief] is not cooperating with us. The Village Counsellor is better. We need a better female Toshao.” “The farming area has changes.” (but couldn’t explain how) “I think it was a bit good.” 1 1 X The villagers were then asked to give other comments about the project, which included criticisms (Table 4.1b). Fifty-­‐seven comments were made with thirteen ‘no answers’ from community members who had not heard of cMRV. The majority of the comments (30) indicated better communication of cMRV was needed. Second highest comment (20), stating frequency and arrangements of cMRV meetings should be improved. 35 Table 4.1b –Comments made by villagers who had heard about cMRV. Themes Sustainable resource use CREW’s work ethic Teaching communities Generally Positive Creates jobs Not enough communication
/education of project Example Quotes “If we don't kill animals/ birds/ fish now then our future generations will see them more than me.” “They should continue doing their work so we will have resource for future generation” “The MRV are working so hard and doing so well, no matter what danger they pass through.” “Susan goes everywhere-­‐ even our satellite village (Simonie).” “(The project) should continue so we can learn more. To teach us.” “It’s a good thing they have passing information to us.” “It’s positive” “I think the project is ok.” “Want to thank MRV, this is good project and good for our community and young people.” “Not all the community know about cMRV, need to speak house to house and tell people about it.” “I don't approach them about MRV I wish I did as I want to know more, they should come round and tell me what they are doing.” Frequency Mentioned 12 7 6 3 2 30 Issues with “If you go to meetings you don't understand properly. People back cMRV meetings talk each other and provoke one another at meetings so you cant ask questions.” “I like going to meetings but cant attend all of them though.” 20 Include more people in communities CREW complaints 8 No payments for information “MRV should include more youths, most people on project are old.” “Its no benefit to me” “MRV workers should be more serious -­‐ one of them was listening to music whilst doing work and taking alcohol.” “The CREWS are willing to receive money but not willing to explain what they have been doing.” “Some say the CREWs ask questions just to get their own money.” 8 2 36 Positive and negative viewpoints per village are set out in Figure 4.2. Surama, Katoka and Apoteri held mostly positive viewpoints. Fairview, Annai and Rupertee held mostly negative viewpoints. 21/70 interviewees held overall positive views about the project, 28/70 overall negative, 9/70 were ambivalent. Kwatamang"
Village$
Rupertee"
Annai"
Surama"
Ambivalent"View"
Katoka"
NegaAve"View"
PosAve"View"
Fairview"
Apoteri"
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No.$of$people$
Figure 4.2 -­‐ Positive and Negative viewpoints held by villagers. 4.1.2 CREW and PMT Impact Evaluations During workshop II, the CREW and PMT anonymously completed the ‘Quiz’ (Appendix 2). When asked: ‘what impact do you feel the project has had?’ 27/30 CREWs felt the project had positive impact. The most common impact themes are highlighted in Figure 4.3; 17 respondents felt cMRV has helped communities understand their resources and sustainable resource management. Three PMT stated the most popular impact shown in Figure 4.3, one PMT stated it provided jobs and one stated cMRV built skills. 37 Making"money"from"comm."knowledge"
Poor"food"and"accomodaFon"
No"coEoperaFon"with"leaders"
People"aware"of"climate"change"
Knowledge"sharing"
More"people"understand"cMRV"
Lack"of"coEoperaFon"with"leaders"
Find"new"friends"
Build"skills"of"those"employed"
Provide"jobs"in"comm."
No"impact"
Understand"sustainable"resource"use."
0"
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No.$of$&mes$men&oned$by$PMT$&$CREW$
Figure 4.3 – Impact themes mentioned by CREW&PMT. (comm=community, orange bars=negative impact, green bars=positive) One CREWs felt they were treated differently to cMRV partners during workshops: lacking suitable food and accommodation, hence negative impact: “poor accommodation and food”. Overall CREW and PMT believe the overall impact of the project is positive. ‘Building capacity within communities to enable sustainable resource management’ is the overarching positive impact. 4.1.3 Key issues of cMRV Figure 4.4 is an example Problem Tree diagram produced during workshop II. Four of five groups highlighted communication and cooperation both within cMRV and between cMRV and communities were major problems caused due to several reasons: •
Lack of internet; •
Distance between CREW and PMT; •
Lack of participation by all CREW at cMRV meetings; •
Not utilising HF radio enough (or correctly) leading to incorrect information being passed on; •
Lack of management by PMT 38 18
Figure 4.4 – An example Problem Tree. 39 Effects of communication problems: •
Logistical problems with accommodation during workshops [not adequate/suitable]; •
Delay of data analysis; delay in reporting to communities; •
Heavy work load for one CREW if communication lacks between them; •
Misunderstanding of the project and criticism of CREWs from communities One group highlighted transportation as the problem within the projects with effects such as delayed data collection and conflict with those who lend vehicles for CREWs to attend workshops/meetings. Highlighted causes are: •
Lack of finance [only one project vehicle and 2 motorbikes purchased at project start]; •
Lack of communication between CREWs and PMT [to organise transportation from villages to meetings/workshops]; This exercise pulled out that communication of and within cMRV is a key problem or cause of another problem. 40 4.1.4 Perceptions of cMRV Objectives There is reasonable understanding of project objectives by CREW and PMT (Figure 4.5), demonstrated by completing the Quiz, which asked: “what are the main aims of the cMRV project, and which ones do you think have been achieved so far?” The most common aim stated by CREW and PMT was “monitoring natural resources”. 20 respondents (19%) included this aim, 18 believed this objective was met. Another repeated aim was “reporting data to communities” (11%), however, this is not an objective but within the conceptual framework as an outcome (Figure 3.2). Nine of believed this outcome was accomplished. 17% believed one objective was to “educate communities in: ecosystem services, climate change impacts, natural resource management and the cMRV” but only 7/17 believed this was achieved. Only three objectives of cMRV were identified between the CREW: •
Develop a long-­‐term monitoring methodology to measure the impacts of REDD+ on the forest and forest-­‐dependent communities. •
Train 32 community members in GPS handhelds and Information Technology for recording and sharing information •
Use data collected from community-­‐based monitoring of forest carbon (and biodiversity, water and wellbeing) to inform the national MRV (LCDS) system. The two objectives not recognised were: •
To develop an internet-­‐based platform to share information with donors about collected forest monitoring data. •
Develop a toolkit for other community MRVs to utilise in order to replicate the cMRV project and benefit from lessons learnt These objectives are ‘outputs’ (the toolkit) and ‘outcomes’ (data being freely available) in the conceptual framework. The Google platform is only being used by GCP; no evidence exists of other stakeholders utilising it. Objective 1 therefore has not been reached to date. Between the PMT all five cMRV objectives were correctly stated with only one objective not believed to be met: “to develop a toolkit for community MRVs (…).” This result is expected as the ‘toolkit’ is currently being finalised by GCP, soon to be published as the cMRV Handbook. 41 Objec&ve:)“Train)32)
community)members)
in)GPS)handhelds)and)
IT)for)recording)and)
sharing)informa&on)by)
end)of)project.”)
Objec&ve:)“Develop)a)toolkit)
for)other)community)MRVs)
to)u&lise)in)order)to)replicate)
the)cMRV)project)and)benefit)
from)lessons)learnt.”)
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Train communities to monitor
Use mobile handholds to monitor
Improve leadership skills in comm.
Learn analysis on data collection
Reporting data to communitites
Comm. knowledge− cMRV
Comm. knowledge− Resources
Comm. knowelde− CC effects
Comm. knowledge− ES
Monitoring natural resources
Conserve our resources
Reduce impact of deforestation
Reduce impact of CC
Monitoring wellbeing
0
No.,of,Respondents,
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Figure 4.5 – Responses given from CREW&PMT Quiz: “What is the cMRV project aiming to achieve?” Respondents’ numbers are individuals who listed theme with percentages given on bars (themes given>100). Actual aims of the project shown in the boxes above and overall cMRV goal=purple box. Purple stars indicate where cMRV goal was stated. SRU=Sustainable Resource Use, FC=Forest Conservation, CC=Climate Change, S=Social, comm=Community, LCDS=Low Carbon Development Strategy. Developed by CS 4.1.5 Local Relevance of cMRV Relevance of cMRV has investigated to discover if it is consistent with country and focal-­‐area priorities. Guyana is developing a framework for REDD+ partnerships and schemes using the LCDS (Office of the President, 2013). Therefore cMRV objectives are relevant to current country prioroties. However, PMT&CREW priorties differ to that of national: 17% of responses, during Quiz: objectives knowledge question, stated ‘educating local communities’ as an objective. The PMT & CREW consist of local people and the objective mentioned highlights local priorities assumed to be achieved with cMRV. Communication and education of cMRV were themes extracted during village interviews. Furthermore, the main project impacts were ‘environmetnal awareness’ and ‘training local communities’ highlighing key local priorities. Although the cMRV project has trained 32 local people, the remaining community have not been targetted and call for increased capacity building, stressing that local priorities have not truly been met. 4.2 Capacity built through cMRV 4.2.1 Villager Knowledge One project outcomes is for information gathered from cMRV to be freely obtainable (Figure 3.2). In order to test for whether communities can freely obtain information about cMRV, villager knowledge of cMRV was investigated and were asked if they had i) heard of cMRV and ii) understood cMRV. Reasoning behind these questions is if someone has heard of cMRV, there is more chance of being able to obtain information on it. During village interviews 57/70 community members had heard of cMRV, yet 25/70 (12 female, 13 men) could explain it (Figure 4.6). 25"
20"
No.$of$respondents$
15"
No."of"respondents"
10"
How"many"can"explain"cMRV"
5"
0"
Under"18"
70+"
30/49"
50/69"
18/29"
Age$Group$
Figure 4.6 – Number of villagers who have heard of cMRV and can explain cMRV. The 50-­‐69 age-­‐range have the lowest proportion of people who knew about cMRV and could explain it. This age group needs to be targeted in future cMRV education and awareness. 44 4.2.2 CREW’s Built Capacity During workshop II, CREW and PMT knowledge on REDD+ and climate change was tested. 21/30 CREW gave the definition of REDD+ but only five CREW gave a precise definition of climate change ie) with a ‘global’ viewpoint. The same questions were asked during village interviewees to act as a control for the training received through cMRV (Table 4.2). Table 4.2 – Results from environmental knowledge testing in PMT, CREW and community interviewees. Group REDD Results PMT CREW Community 4/4 21/30 6/70 Climate Change Results 2/4 5/30 12/70 There was no significant difference between CREW’s knowledge on climate change and community’s (𝜒2 =0.003, df=1,P=0.96). However, there was a significant difference between CREWs and community’s ability to explain REDD, where the CREW have better understand of REDD (𝜒2=40.204, df=1, P<0.001). The repeated definition used by CREW and PMT was: “climate change is the change in normal weather pattern over an extended period of time.” Although this definition is still correct, they have not shown the same understanding as those community members who explained climate change to have global effects. The reason the definition above is repeated may be due to the different definitions given during training sessions (Box 4.1). Box 4.1 – Definitions of climate change given in Conservation International’s community manual Climate: is described as “the average weather” or weather conditions that happen over a long period of time. Climate change: the change of the normal weather patterns around the world over a long period of time. (Stone & Chacón León, 2010) 45 During evaluation workshop I, an Honesty Box exercise was used to assess CREWs built capacity, testing the outcome: “community forest monitors are employed and actively engaged in regular, reliable (audited) monitoring activities”. Out of 26 CREWs present, five stated they had not received enough training to do their job called for additional training and recaps. Three were unsure, one of these also asking for follow-­‐up training. However, 18/26 present CREWs confirmed they received enough training to do their job. 4.2.3 Technical Problems To accomplish one of the cMRV project outputs: “32 trained community members”, a technology question was included in the CREW quiz. In answer to the question: “how well do you feel you can use the mobile device”. Out of 4 PMT, one answered “Outstandingly well”, two answered “Really well” and one answered “Quite well”. From the 30 CREWs present: •
5 = “Outstandingly well” •
11 = “ Really well” •
10 = “Quite well” •
4 = “Not so well” •
0 = “Can’t use it” Table 4.3 highlights some problems prevalent with the handhelds-­‐ used for GPS and monitoring of natural resources and human wellbeing. 46 Table 4.3-­‐ Results from CREW and PMT technical question of Quiz Technical Problems Battery Power No. of people who encountered problems 7 GPS 5 Lack of Internet (to download forms/email) Forget what was taught 4 Can’t see the screen clearly 2 Storage Capacity 1 No problems 8 4 References from Quiz “During fieldwork battery power finishes too fast” (CREW) “Main problem was recording GPS in high forest” (CREW) “Don’t have the internet to down load forms (…)” (CREW) “Don’t know how to download my data” (CREW) “(…) can’t see well with sun reflecting on screen” (CREW) “(…) and storage capacity” (PMT) -­‐ Considering the CREWs capacity built through cMRV and that CREWs are employed part-­‐time highlights the outcome: “Community forest monitors are employed and actively engaged in regular, reliable (audited) monitoring activities” has been achieved. The input of “32 community members trained” has also been accomplished as all CREW (out of 30) can use mobile devices at least ‘quite well’ for monitoring resources. Training evidence is shown in GCP training session attendance records (M. Menton, Pers. Comms3.). Although there is evidence that at least 30 community members have been trained, there is no quality control to test whether the training’s outcome is qualified community forest monitors. More training and testing on climate change, monitoring and technology is needed and has been requested by some CREWs as well as community members where some have stated CREWs should be better selected and tested before becoming a CREW. 3 Mary Menton is the head researcher at Global Canopy Programme for the cMRV. 47 4.3 The ideal cMRV champion This section will look into the existing strength of relationships the CREW and their villages have and whether information on cMRV and its importance is effectively and appropriately reported to the communities. 4.3.1 Presence of CREW in Villages During village interviews social network questions were asked to identify key members in each community (Appendix 3). Interviewees provided lists of who they i) speak to the most socially, ii) trust the most in the village and iii) respect the most. Cultural salience scores were calculated for all mentioned villagers. 1 is the highest salience, where that person is listed top from all respondents and 0 indicates ‘no salience’ and was never mentioned by interviewees. Within study villages one CREW, from Fairview, appeared within the top three communicators of village, with a cultural salience score of 0.21 (Figure 4.7b). At least one CREW in each village was mentioned by at least one member of the sample village population within their closest trusted, respected or communicable circles. Figures 4.7a-­‐d show examples where CREW were mentioned within the three areas of social standing to put the CREW’s cultural salience in context with other community members. 0.7"
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Figure 4.7d – Annai-­‐Central Village Respect Salience (Mean=0.09, SD=0.11) The SD in Annai-­‐Central is higher in comparison to other villages. The Toshoa is granted most respected by the majority of interviewees. The ideal state for a cMRV champion would be one who is trusted and respected by their community and who is socially active in communicating with a variety of different people. The individuals with the highest salience scores in all three areas of social standing have been grouped by professional type/village role (Figure 4.8). These village roles will highlight to cMRV where ideal cMRV champions can be found. Village"Councilor"
Toshoa"
Health"Worker"
Formally"employed"
Pastor"
Teacher"
Cook"
Member"of"Parliament"
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Figure 4.8 – Occurrences of villagers who hold the highest cultural salience scores in their village 50 Numerous interviewees mentioned village councilors, as their cultural salience was high in each study village, highlighting that village councilors have most social influences on their community. To date, Toshoas have had most involvement with cMRV and invited to general meetings at Bina Hill Institue. The only regular village member with high cultural salience was a cook (Figure 4.8). 4.3.2 Factors influencing information spread Distance away from BHI was explored to test for correlation with village understand of cMRV. A General Linear Model (GLM) tested for any relationship between two variables: distance (measured in time taken to get to BHI from villages) and village understanding (Figure 4.9a). 10
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Figure 4.9b – Number of village respondents with good understanding of cMRV in relation to CREW cultural salience scores per village 4.3.3 Factors effecting village attitudes This section will go through different causation possibilities of what effects positive village attitude towards the cMRV project. Subjective attitude scores were given to each village depending on the overall feel from each respondent. CREW salience was correlated with village attitude scores first to investigate any correlation between these two variables (Figure 4.10a). 52 6#
4#
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Figure 4.10a – The effect of CREW salience within communities on village attitude towards the project. Kwatamang, Rupertee and Annai show that low village salience scores correlate with an overall negative village attitude towards the project. Although Apoteri and Surama have low salience scores, their village attitude scores are high. There is no correlation between village distance from BHI and village attitudes. The furthest village (Apoteri) holds the highest village attitude score and the closest village (Annai-­‐Central) holds the lowest village attitude score (Figure 4.10b). Fairview is the only village where an increased time travelled from BHI institute correlates with a low village attitude. 53 6#
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Figure 4.10b –Village distance from BHI correlated with village attitude towards The three nearest villages hold the most negative attitude towards the project. Apoteri and Katoka have high attitude score and also highest standard deviation across their villager cultural saliences, where most people in the village see one or two figures as most trustworthy/respectable/communicable. Therefore, if that person (in Katoka: the Pastor and Toshoa and in Apoteri: the Health Worker) holds more positive views this message will be passed on to the community. Annai-­‐Central also hold a high SD with the Toshoa being the clear village champion (Figure 4.7f), however their village attitude towards the project is the lowest. During a general cMRV meeting the Toshoa of Annai-­‐Central announced there are land-­‐use tensions between Annai-­‐Central and other villages within Annai-­‐District caused by the cMRV mapping exercise, which mapped village resources. Annai-­‐Central is not situated near forests, rivers or farms and traditionally all villages of Annai-­‐District shared resources. The new maps make it clear where village resources are located, causing tension with Annai-­‐Central. This verifies the necessity of recognizing, building capacity and utilising village champions as they are important players and potential advocates of local projects. 54 4.4 Social Sustainability of cMRV project In order to evaluate the likelihood of long-­‐term social sustainability of cMRV, social network analysis (SNA) was used to determine which project partners (GCP, Iwokrama and NRDDB) and project staff (CREW and PMT) are vital for spreading information and influencing the opinions of others. An overall well-­‐connected (dense) network allows more potential for communication, collaboration and the exchange of ideas between people (Prell et al., 2011), creating social sustainability. The present network structure (Figure 4.11) illustrates the position of partners and staff of cMRV in the network. The network density of cMRV is 0.69 (maximum=1). This is calculated from actual links divided by potential links. Hawe et al. (2004) showed a network density of 0.164, was calculated from 56 ties out of a potential 342. Compared to this, the network density of cMRV can be classed as high, therefore the network has the potential for high levels of communication and collaboration between the partners. Statistical tests of betweenness and degree centrality measures were run to investigate significant differences between the communication and influencing abilities of cMRV partners and project staff. Particular attention is paid to the role GCP play in the network. For cMRV to continue long-­‐term, GCP should play a peripheral role in the network to encourage self-­‐dependence. 55 Figure 4.11 – cMRV network plot using communication frequency as a proxy for connectedness within the network. Node numbers are unique codes of the 53 individuals involved in cMRV.
4.4.1. Individuals’ controlling information flow between others in cMRV Betweenness scores were calculated for all individuals in the network using frequency, type and reason for communication. Centrality scores were standardised between 0-­‐1. Frequency of Communication An associate of GCP has the highest betweenness centrality score (1), second highest is 0.78. This highlights that those individuals are well connected and lie between many people in the network. These are key individuals to remain in the network to link different groups with eachother and spread information to other groups. The lowest betweenness scores was a CREW-­‐member, PMT and NRDDB member who all scored 0, indicating they do not lie on the shortest path between any pairs of nodes, therefore have no control over information flow to anyone they are not directly connected with in the network. Type of Communication Type of communication was weighted with face-­‐to-­‐face and a combination of communication medians as the highest, with email as the lowest with telephone/HF Radio in-­‐between. The highest betweenness scores were two PMT staff (1) and (0.66), which is expected due to their access to the HF radio and internet at Bina Hill. These PMT members are most important for spreading information to different groups within the network and are important to 1) keep in the network and 2) be given the correct information to spread. The lowest was a member of GCP (0) and nine CREW also scored 0, indicating their absence of lying between groups of individuals. Reason for Communication One PMT scored the highest (1), a member of GCP scored second highest (0.88) and another PMT scored third (0.77). These individuals communicate about the most variety of topics and lie between groups in the network therefore can spread information to more people who otherwise would not be connected with. A member of GCP, CREW and PMT held the lowest scores (0), indicating their inability of linking other in the network. 4.4.2. Individuals’ influencing power within the cMRV network Using the frequency, type and reason of communication plots the degree centrality was calculated and set out below. Degree centrality calculates individual’s capacity to communication with the most people, a popularity score. Frequency of Communication The network member with the highest degree centrality score was part of the PMT. Followed by three other PMT members with the next highest scores (0.98, 0.97, 0.95). This shows that the PMT members are most central with their communication frequency in the network and have the ability to communicate with the most amount of people, thus most powerful in influencing people within the network. The lowest scoring network member in communication frequency is part of Iwokrama (0.1). The second lowest member of the network is part of GCP (0.25). Type of Communication A PMT staff scored the highest in communication type (1), followed by three other PMT members (0.96, 0.95, 0.93). This shows PMT members communicate in the most ways to the most amount of people within the network. They not only lie between groups but also talk to the most people. They are powerful in influencing the most amount of network members. The lowest scoring members are from Iwokrama (0.14) and GCP (0.25) thus have the least ways of communication with people. Reason for Communication The same PMT was also the network member who scored the highest in communication reason and frequency, followed again by three other PMT-­‐staff and one CREW (0.96, 0.96, 0.95, 0.92). Eight other CREW also scored high (0.90, 0.89 and 0.88). These members talk about a large range of subjects to the most amount of people, therefore can highly influence the most people, thus important to the network and cMRV sustainability. Lowest scores are one member from Iwokrama(0.1) and GCP(0.25). 58 4.4.3 Role of groups in spreading information within the cMRV Partners and staff of cMRV were compared using the same centrality measures to test for which groups in total are most influential to the network. Frequency of Communication The two plots in Figure 4.12 are different for degree and betweenness showing that although Iwokrama are lowest in degree for communication frequency, they are highest in betweenness. A low degree score means they are directly connected to less people but the high betweenness score means the people they are connected with are well-­‐connected. Although they do not directly influence people, they have the ability to influencing the information being spread between groups. It is the CREW and PMT that have the ability to directly influence the most people in the network as they are directly connected to more. Although GCP have one of the highest betweenness individuals in the network, the difference between GCP and Iwokrama’s group scores is not significant, therefore Iwokrama and PMT could potentially take on this communication role. 6
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Figure 4.12 – Box and Whisker Diagram showing degree and betweenness centrality partner and staff scores for communication frequency. 59 PMT
For degree centrality in communication frequency an ANOVA was run to test for significance between partners and significance was present (FValue=9.17, PValue<0.001, d.f=4). Tukey post hoc test found significant difference between several partners: GCP–CREW (p=0.016,diff=-­‐0.44), Iwokrama–CREW (p<0.001,diff=-­‐0.77), PMT–GCP (p=0.0058,diff=0.61), PMT–Iwokrama (p<0.001, diff=0.95). For betweenness centrality in communication frequency an ANOVA was run. Significance was found between betweenness scores of the partners (FValue=2.824, PValue=0.035, d.f= 4). A Tukey post hoc test found no correlation between the partners, however only NRDDB-­‐GCP was close to significance (p =0.096, diff=-­‐2.34). A Wilcox test found a slightly more significance between NRDDB-­‐GCP(p=0.06). Reason for Communication Communication reason has been used as proxy for social connections, where the more reasons for communicating with one person provides a higher connectedness score (Figure 4.13). For example, if two people talked about business their relationship would be weighted less than the two people who talked about advice, or business and social together. For the communication reason it may be more important to target the person with the highest degree scores as that person will have the ability to influence most people’s opinions in the network. 5
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Figure 4.13 – Box and Whisker Diagram showing degree and betweenness centrality partner and staff scores for communication reason. 60 For degree centrality in communication reason an ANOVA found significant difference between partners (Fvalue=12.68, P<0.001, 4d.f). A Tukey post hoc test found significant difference between several partners: GCP–CREW (p=0.001,diff=-­‐28.1), Iwokrama–CREW (p< 0.001, diff=-­‐37.9), PMT–GCP (p<0.001, diff=44.2), PMT–
Iwokrama (p<0.001,diff=54.1), PMT–NRDDB (p=0.02,diff=30.1). For betweenness centrality in communication reason ANOVA shows no significant difference between groups (FValue=1.009,PValue=0.412, d.f=4). A similar pattern is occurring in reason of communication as with frequency, however Iwokrama are poor at both directly influencing (degree) and information spreading (betweenness). It is the PMT and CREW who have the ability to influence the most people in the network thus important to have the right information to begin with and to remain in the network. Type of Communication In type of communication (Figure 4.14) the CREW scored significantly lower betweenness scores than the PMT and lower than other partners and staff (expected due to lack of internet in villages). The CREW are linked to less people who are connected through more communication mediums. This could affect the amount of information they can pass on to communities, ultimately affecting village attitudes towards the project. However, with degree CREW are significantly higher than GCP and Iwokrama in communication type so directly use more ways of communicating with network members than GCP and Iwokrama. This shows CREW have potential to use more ways of communicating within the cMRV network. 61 5
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Figure 4.14 – Box and Whisker Diagram showing degree and betweenness centrality for partner and staff scores for communication type. For degree centrality of type of communication an ANOVA test found significant difference between CREW and other groups (D.F=4, FValue=11.39, PValue<0.001). Tukey post hoc test found significant difference between the partners: GCP–CREW (p=0.001,diff=-­‐28.1), Iwokrama–CREW (p<0.001,diff=-­‐37.9), PMT-­‐GCP (p<0.001,diff=44.2), PMT–Iwokrama (p<0.001, diff=54.1), PMT–NRDDB (p=0.02,diff=30.1). For betweenness centrality the ANOVA showed a significant difference (FValue=6.115, PValue<0.01, d.f=4). Tukey post hoc test found the only significant difference occurred between the PMT–CREW (p<0.001, diff=63.5). In all three parameters (type, reason and frequency), the CREW have higher degree scores than betweenness, which uncovers they are directly connected to more people but the people they are connected to are less connected. Emphasising that more integration of partners is needed within the network so that information flow can be spread between partners and not just between themselves. 62 5. Discussion 5.1 Evaluating a community-­‐based monitoring project The overarching goal of the cMRV project is to empower forest dependent communities in order for them to take advantage of future REDD+ incentives (Appendix 1). The outcomes and outputs of the project outlined in the cMRV conceptual framework are indicators for whether this goal has been reached. As already discussed, objectives should be ‘SMART’ in order to measure their effectiveness (Schroeder, 2009). Using the conceptual framework developed for the cMRV, the key outcome: “improve quantity and quality of community forest monitoring” is yet to be achieved, thus the conservation impact would not have been reached. Although the overall impact of the project to local communities was positive, many comments and improvements were made and should be taken into account. Focus of conservation projects and their evaluation should shift from “inputs” and “outputs” to “outcomes” (Ferraro & Pattanayak, 2006, GEF, 2008;2010). Similar findings are demonstrated in this study, with project outputs reached but not all project outcomes accomplished. Furthermore objectives were not clearly outlined from the beginning and were not developed with all stakeholders and communities thus misconceptions existed of what the project outcomes were. It is recommended that Integrated Conservation and Development Programmes (ICDPs) should incorporate stakeholders at all levels of a project, including defining the objectives and the indicators used to assess them (Mistry et al., 2010). This study has added to the literature on the importance of community-­‐based monitoring in involving local communities to reach a conservation goal. 5.1.1 Adaptive management in community-­‐based conservation An evaluation on the impact and effectiveness of a project supports the fundamental definition of adaptive management of systematic learning and adapting (Salafsky et al., 2001). Continuous evaluations should be undertaken during project implementations as well as post. The objective most clearly obtained was the training of 32 community-­‐forest monitors, with adaptive management practiced throughout. 63 This was demonstrated by in-­‐country Project Management Team dealing with technical problems with CREW and several training sessions held for CREWs (M.Menton, Pers Comms). For small community-­‐monitoring schemes adaptive management is still relevant, however this can be time consuming. Using a conceptual framework and results chain such as those outlined by CCF (Kapos et al., 2009; 2008) helps visualise and systematically test whether outputs and outcomes have been achieve, which ultimately leads to the conservation impact. However, clear objectives should be defined through a participatory process and set out in a conceptual framework in order to measure their achievements from the beginning (Mistry et al., 2010). Project staff identified some project objectives, but community capacity building objectives were interpreted as project objectives, thus not supporting Mistry et al., (2010) and Wells & McShane (2004), who emphasise the importance of participatory objectives. Setting participatory objectives ensures project aims are clear and over-­‐optimistic goals are avoided, leading to clearer expectations of a conservation project. 5.2 Community monitors and their presence The importance of project “champions” within villages is paramount to spreading information about conservation projects (Knight et al., 2010). Champion attributes includes capability of building social capital (Knight et al., 2010), which encourages cooperation (Pretty, 2003). In remote areas, especially those geographically isolated from main roads, such as Apoteri, it is even more pertinent that social capital and communication are increased within village to ensure project information is correctly spread, as less information from external sources will reach community members (Wu & Pretty, 2004). Using results set out here, cultural salience within villages can be used to identify key champions who have high social standing within communities in order to spread the project’s conservation message. Using project champions as stakeholders, community-­‐
based monitoring programmes will increase local participation and increase the 64 likelihood of project success (Mistry et al., 2010). This compliments comments made by Brooks et al. (2012), who demonstrates increased success of conservation projects when there is a higher investment into human and social capital through capacity building. A rigorous selection process of local forest monitors is essential to reflect the comments given by the community members. One interviewee stated: “there should be better selection of CREWs -­‐ some don’t have very good schooling so they can’t tell you exactly what is going on with MRV”. In the cMRV, potential CREWs were recommended from each village council (L.Hayes, Pers.Comms4). Furthermore, quality controls to test whether the CREWs have passed on the correct information to their communities were not in place. The CREW positions should, in future, be advertised in which villagers can apply and go through a selection process in order to lessen community uncertainties in CREWs capabilities. 5.2.1 Effective Communication Spread A high proportion of villagers had previously heard of cMRV compared to a much lower proportion (25/70) understanding what the project is trying to achieve. This suggests communication spread has not been as effective as it could be. All those who heard of the project have the capacity to understand the project as social structures and communication paths are already set up within villages but the relevant information is not using those paths. During this study, communication of the project relied solely on community-­‐forest monitors to relay correct information. Strong cultural salience of CREWs within villages is essential in order to champion a project’s cause. As Papworth et al. (2013), describes, a high cultural salience of an individual has high importance to the studied culture of a community. Therefore community-­‐forest monitors should ideally have a high cultural salience. The results here show only one CREW fits this ideology. 4 Lakeram Hayes currently works on COBRA project at Bina Hill Institute and is a local resident to Kwatamang village. 65 Other key community members with a high cultural salience have been described as potential local ‘champions’ of a project and have been identified within this study as being respected, trusted and socially involved with the majority of their communities, which supports work by Sukhdevb et al. (2012) and Knight et al. (2010). In order to identify these key champions it is suggested here that salience can be integrated alongside social network analysis (SNA) and should be undertaken pre-­‐project to include those champions as stakeholders of the project. This process is supported by Prell et al. (2009), who states SNA should be used as a stakeholder analysis rather than relying on subjective assessments of who holds relative power and influence in the local area. Results here suggest no correlation between cultural salience of CREW and knowledge of the cMRV project, and no correlation with attitude of the project. This suggests the CREW may not be the correct person for the spread of information, or do not have the capacity to be this champion as of yet. Other unknown underlying factors may be affecting the knowledge spread of cMRV, which needs further investigation. 5.2.2 Importance of community characteristics and strong leaders Community characteristics are shown to be important to the success of community-­‐
based conservation (CBC) projects (Brooks et al., 2012). Mehta & Heinen (2001) also conclude that a CBC approaches improves local attitude towards the conservation goal which increases local support for the project. This study found that although negative comments were made for improvements of the project, the overall attitudes towards cMRV was positive, which confirms the findings from Mehta & Heinen (2001) and Brooks et al. (2012). An understanding of local opinions and acquiring local support can determine project failure or success (Brooks et al., 2012). The effect of both village isolation and strong leadership have been highlighted by Brooks et al, (2012). Apoteri, the furthest village has shown to have the most positive attitude of the project, which could be due to a strong institution. With a high cultural salience and standard deviation, the village collectively respects the village Health Worker. 66 Mistry et al. (2010), explains that the North Rupununi has low local participation levels due to the lack of confidence from communities to make long-­‐term commitments for natural resource management, especially as land tenure is currently limited to their immediate settlement vicinities rather than their traditional land use areas. Brooks et al. (2012) also emphasises this point, where the local context is important to understand and can effect conservation success. Village leadership is an essential characteristic where strong leadership collates with project success (Brooks et al., 2012). Most village results demonstrated this finding as village salience scores with strong leaders held more positive views of the project. One village (Annai-­‐
Central) contradicts Brooks’ et al. (2012) finding as although strong leadership was present in the salience scores, the overall view of the project was negative. Other community characteristics and local context play a significant role in the ability of successful outcomes, especially supportive local belief systems, strong traditional management institutions and a culture of community cooperation. The historical context of community collaboration with projects was not within the scope of this study but would be pertinent to investigate whether communities are more likely to cooperate with a project from the beginning. 5.2.3 Building Capacity Danielson et al. (2009) highlights the need for local expertise is more substantial the more involvement communities have in the project. He defines categories of community involvement. Currently the cMRV is a category 3: collaborative monitoring with external data interpretation. The aim for cMRV is local data interpretation (category 4) and in the future autonomous local monitoring (category 5). For these levels of community involvement to occur, intensive capacity building of an increased number of local people is needed to ensure there are enough locally skilled people capable of interpreting data, selecting relevant monitoring methods and making management decisions (Danielson et al., 2009). The communities in the North Rupununi have knowledge of climate change, the underlying reasons global warming occurs and the effects it could have on them. One 67 villager stated: “we cannot predict the rains anymore which means we risk losing our crops more”. Currently, only the village leaders (Toshoas) and two community monitors per village are stakeholders within the project. Mistry et al. (2010) suggests as many local stakeholders should be included in the decision-­‐making and capacity building as possible, complimenting Danielson et al, (2009). Public education and capacity building have been highlighted as locally important objectives but were not set out as project objectives. Capacity building and education is often by-­‐passed during conservation interventions (Kleiman et al, 2000) but remain integral to reach the conservation goals. 5.3 Social Sustainability of cMRV Analysing social networks of project partners and stakeholders pre-­‐implementation allows for prior knowledge of the type of management needed and key players within that network who are responsible for information spread and influencing others (Degenne & Forse, 1999). Using centrality measures individuals within the network can be identified for fulfilling different tasks (Prell et al., 2009). In the cMRV network those with a higher centrality score were able to spread information through the network but Prell et al. (2009) suggests these individuals are also good at motivating the network and bringing people together. This can inform the type of capacity that needs to be built within the network, as some individuals may be better used for communication and others for management. Building capacity for best practices in spreading information and communication is essential for more ‘central’ individuals: in this study, the Project Management Team. Although there is no perfect network structure for all community-­‐based conservation projects, there is a difference between aiming to change cultural norms or spreading information (Hawe et al., 2004). For the cMRV to reach their overall aim of empowering local communities, this calls for spreading information. The project has a dense network, giving it the ability to fulfill the aim. A loose-­‐knit network is more appropriate for changing cultural norms within the network’s social groups. More connection between partners and project staff is essential to bridge the gap between levels of project management and increase stakeholder participation 68 throughout the project. This was also highlighted from CREWs who brought up the indifference between project partners and CREW. Segregation between groups creates a feeling of separation and hierarchy between project stakeholders and partners, which should be avoided. 5.4 Future research and recommendations Evaluations should continue throughout the cMRV project and future project life spans, using the conceptual framework set out as an aid in answering whether the key outcome has been achieved: improved quantity and quality of community forest monitoring (Figure 3.2). Indicators for when this key outcome has been reached should be developed collaboratively through a participatory process. Comments from villagers regarding outreach and meetings and ways they can be improved should be taken into account to ensure information spread is more holistic in each village. The infrastructure for increased education and outreach of cMRV is already in place in the North Rupununi with Paramount Radio Station recorded and broadcasted from BHI. Weekly educational radio sessions are broadcasted but are always looking for more material (V.Harding, PersComms5). Similar suggestions from CREWs was to make a short film/documentary about the cMRV project and its importance. Most households have a television with either a DVD/CD player thus capable of playing a cMRV distributed DVD. Project objectives should incorporate more capacity building and education within communities to support findings from this study, Mistry et al. (2010) and Danielson et al. (2009). This will make objectives more locally relevant and consistent with expectations of both CREW and communities. Finally, collaborations with other projects in the area are vital to share lessons learned between one another. ‘COBRA’ project currently undertake similar interviews in the same villages and often interviewees assumed cMRV was the same project as COBRA. This also causes interviewee fatigue: a serious problem in some villages 5 Vigil Harding is Radio Paramount’s only DJ and works on the daily programmes and music broadcasted 69 (V.Antone, PersComms6). COBRA use participatory videos and storyboards to document and share the stories of local people and best practices they use for sustainable resource management, yet to date there has been no collaboration between the two projects. 5.5 Limitations Time constraints were prevalent: all 16 villages could not be visited in the time frame therefore study villages were allocated. Only two days were allocated per village to account for travel time in-­‐between. Four to six interviews were completed each day, which enabled a sample size of ten per village. A statistically stronger sample size in each village would be beneficial if time limitations were not present. Interviews were conducted during the day, which restricted the amount of interviews conducted with working people therefore those working away from home or on farms may be under-­‐represented. Some village elders spoke local dialects, which CREW translated during interviews. A briefing of interview protocol was given by CS to ensure questions were not loaded or leading as advised by Bernard (2011), however this was difficult to regulate. 5.6 Concluding remarks This study has assessed some of the impacts of a community-­‐based monitoring scheme in Guyana using participatory methods and social network analysis in which highlights areas of improvement for not only the cMRV project but for community-­‐
based monitoring and conservation projects globally. Married with education, community-­‐based conservation can lead to successes of conservation efforts (Kleiman et al., 2000). The importance of communication and education has been illuminated throughout this evaluation and remains an essential component of successful community-­‐based conservation projects. When properly designed and nestled into traditional management structures, community-­‐based monitoring schemes can survive longer 6 Vitus Antone is the Project Co-­‐ordinator for the cMRV Project Management Team. 70 than professional ones (Danielson et al., 2005). Communication and collaboration are not only important for conservation but for communities’ ability to act collectively and adapt to climate variability (Adger, 2003) and political pressures. 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The Geographical Journal, 176(3), 241–252 Mistry, J., Simpson, M., Berardi, A., & Sandy, Y.(2004).Exploring the links between natural resource use and biophysical status in the waterways of the North Rupununi, Guyana.Journal of environmental management, 72(3), 117–31. Mitchell, R.K., Agle, B.R., & Wood, D.J.(1997).Toward a Theory of Stakeholder Identification and Salience: Defining the principle of who and what really counts.The Academy of Management Review, 22(4), 853–886. 76 Mittermeier, R.A.(1988) in Biodiversity (eds Wilson, E.O.& Peter, F.M.) 145-­‐154 National Academy Press, Washington, DC Newing, H., Eagle, C.M., Puri, R.K., & Watson, C.W.(2011).Conducting Research in Conservation.A Social Science Perspective (p.399).Routledge Taylor & Francis Group, 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN. Office of Climate Change.(2012).Focus on the LCDS: Guyana’s Low Carbon Develpment Strategy. Georgetown,Guyana. Office of the President, R.of G.(2013).Transforming Guyana’s Economy While Combating Climate Change. Georgetown,Guyana Palmer Fry, B.(2011).Community forest monitoring in REDD+: the “M” in MRV? Environmental Science & Policy, 14(2), 181–187. Papworth, S., Milner-­‐Gulland, E.J., & Slocombe, K.(2013).The natural place to begin: The ethnoprimatology of the Waorani. American Journal of Primatology, 9999:1–12 Parker, C., Mitchell, A., Trivedi, M., Mardas, N., & Sosis, K.(2009).The Little REDD+ Book.Oxford: Global Canopy Programme. Parry, J.T., Eden, M.J.(1997) Monitoring and managing land degradation in Guyana: the future.In P.E.Williams, J.T.Parry and M.J.Eden (Eds.), Land use, land degradation and land management in Guyana (pp.93-­‐103).Commonwealth Geographical Bureau: University of London,Surrey Pirard, R., & Treyer, S.(2010).Agriculture and deforestation: What role should REDD+ and public support policies play? Institute for Sustainable Development and International Relations(10). Prell, C., Hubacek, K., & Reed, M.(2009).Stakeholder Analysis and Social Network Analysis in Natural Resource Management.Society & Natural Resources, 22(6), 501–
518. Pretty, J.(2003).Social capital and the collective management of resources. Science,302(5652), 1912–4. Pritchett, L., Samji, S., & Hammer, J.(2012).Working Papers, (249). Quinlan, M.(2005).Considerations for Collecting Freelists in the Field: Examples from Ethobotany.Field Methods, 17(3), 219–234. R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Redford, K.H., & Taber, A.(2000).Writing the Wrongs : Developing a Safe-­‐Fail Culture in Conservation.Conservation Biology, 14(6), 1567–1568. 77 Richards, M., & Panfil, S..(2011).Towards Cost-­‐Effective Social Impact Assessment of REDD+ Projects: Meeting the Challenge of Multiple Benefit Standards.International Forestry Review, 13(1), 1–12. Richardson, K.S., & Funk, V..(1999).An approach to designing a systematic protected area system in Guyana.Parks, 9(1), 7–16. Rossi, P.H., Freeman, H.E., & Lipsey, M.W.(1999).Evaluation: A systematic approach.Thousand Oaks, CA: Sage Publications. Salafsky, N., Margoluis, R., & Redford, K.(2001).Adaptive Management : A tool for Conservation Practitioners.Biodiversity Support Program (BSP) p.53. Washington, D.C. Sayer,. J., Campbell., B.(2004) The science of sustainable development Cambridge University Press, Cambridge Scholz, J.T., Wang, C.-­‐L.(2006).Cooptation or transformation? Local policy networks and federal regulatory enforcement. American Journal of Political Science 50, 81–97. Scholz & Schmidt (2008).“Reducing Emissions from Deforestation and Forest Degradation in Developing Countries: Meeting the Main Challenges Ahead.” Deutsches Institut für Entwicklungspolitik. Schreckenberg, K., Camargo, I., Withnall, K., Corrigan, C., Franks, P., Roe, D., … Richardson, V.(2010) Social assessment of conservation initiatives Social assessment of conservation initiatives: A review of rapid methodologies. Natural Re., IIED, London Schroeder, R.L.(2009).Evaluating the Quality of Biological Objectives for Conservation Planning in the National Wildlife Refuge System.The George Wright Forum, 26(2), 22–
30. Scott, J.(2013).Social Network Analysis.(K.Metzler, Ed.) (Third., p.201).London: Sage Publications. Skutsch, M., Van Laake, P.E., Zahabu, E., Karky, B.S.and Phartiyal, P.(2009).Community monitoring in REDD+.Chapter 8 in: Angelsen, A.(ed).Realising REDD+; National strategy and policy options.International Centre for Research in Forestry (CIFOR), Bogor, Indonesia. Skutsch, M.M., & Ba, L. (2010) Crediting carbon in dry forests: The potential for community forest management in West Africa, Forest Policy and Economics. Smit, B., Burton, I., Klein, R.J.T., & Wandel, J.(2000).An anatomy of adaptation to climate change and variability.Climate Change, 45, 223–251. 78 Stem, C., Margoluis, R., Salafsky, N., & Brown, M.(2005a).Monitoring and Evaluation in Conservation: a Review of Trends and Approaches.Conservation Biology, 19(2), 295–
309. Stem, C., Margoluis, R., Salafsky, N., & Brown, M.(2005b).Monitoring and Evaluation in Conservation: a Review of Trends and Approaches.Conservation Biology, 19(2), 295–
309. Stone, S., & Chacón León, M.(2010).Climate Change and the Role of the Forests.A Community Manual (p.70). Sukhdevb, P., Ravi, P., Pushpam, K., Andrea, B., Wahida, P.-­‐S., Thomas, E., … Julie, G.(2012).REDD+ and a Green Economy: Opportunities for a mutually supportive relationship.Journal of health and social behavior (Vol.53).Geneva, Switzerland. Ter Steege, H., Zagt, R., Bertilsson, P., Singh, J. (2000) Plant diversity in Guyana: implications for the establishment of a Protected Areas system. In: Ter Steege, H.(Ed.), Plant Diversity in Guyana. With Recommendation for a Protected Areas Strategy.Tropenbos Series 18.Tropenbos Foundation, Wageningen, The Netherlands.pp.159–178. The World Bank.(2004).Sustaining Forests.A Development Strategy (Vol.4, p.299).Washington, D.C. Thompson, M.C., Baruah, M., & Carr, E.R.(2011).Seeing REDD+ as a project of environmental governance.Environmental Science & Policy, 14(2), 100–110. UNFCCC.(2011).Report of the Conference of the Parties on its sixteenth session , held in Cancun from 29 November to 10 December 2010 (pp.1–31). Wells, M.P., Brandon, K.(1992)People and Parks: Linking Protected Area Management with Local Communities.The World Bank, U.S.Agency for International Development and World Wildlife Fund, Washington, DC Wells, M.P., & McShane, T.O.(2004a).Integrating protected area management with local needs and aspirations.Ambio, 33(8), 513–9. Wells, M.P., & McShane, T.O.(2004b).Integrating protected area management with local needs and aspirations.AMBIO: A Journal of the Human Environment, 33(8), 513–
9. Wetlands Partnership.(2008).The North Rupununi Adaptive Management Process ( NRAMP ).Georgetown.Guyana. Wilder, L., & Walpole, M.(2008).Measuring social impacts in conservation: experience of using the Most Significant Change method.Oryx, 42(04) Wu, B., & Pretty, J.(2004).Social connectedness in marginal rural China: The case of farmer innovation circles in Zhidan, north Shaanxi.Agriculture and Human Values, 21(1), 81–92. 79 Appendices Appendix 1: GCP Project Goals and Objectives Overall Goal: Empower forest-­‐dependent communities in Guyana to benefit from future REDD+ payments through community-­‐based forest measuring, reporting and verification of biodiversity, ecosystem services and human wellbeing. Sub-­‐goals: 1. To develop an internet-­‐based tool (Google Earth) to demonstrate to external actors (e.g. donors) the value of their forests’ ecosystem services and their role as forest stewards. 2. To build on existing initiatives (that are developing tools and methodologies to help communities manage their forest resources and ecosystem services and be better able to understand and adapt to climate change) by training communities in the use of GPS handheld data recorders and Information Technology tools for recording and sharing information on the internet. 3. To link community-­‐based monitoring of forest carbon and co-­‐benefits (biodiversity, water and wellbeing) to the national Measurement, Reporting and Verification (MRV) system. 4. To create a methodology that can, over the long-­‐term, monitor the impacts of REDD+ on the forest and forest-­‐dependent communities. 5. To provide a Community-­‐MRV model that can be replicated in other parts of Guyana and countries of the Guiana Shield and Amazonia. Indicators: 6. A community-­‐based monitoring protocol is developed, that builds on and complements existing monitoring, through a process of broad consultation with stakeholders in local communities and government. 7. Community forest monitors are actively engaged in regular, reliable (audited) monitoring activities. 8. Data obtained by the community monitors is available to the local community and, as agreed, to the Government of Guyana, donors and those investing in the Iwokrama’s ecosystem services on a Google Earth platform. 9. Governance system for management of transfer of information to public websites developed. 10. 32 community members have increased capacity to use information technology such as GPS, internet and GIS (through synergy with existing capacity-­‐building projects) and are employed as community forest monitors. 11. The national MRV system and REDD+ policymaking recognise and use information gathered by communities. 12. Community-­‐MRV is included in the national REDD+ strategy. 13. Community-­‐MRV is included in good practice REDD guidance. 14. Communty-­‐MRV systems and lessons for optimizing co-­‐benefits methods are shared with similar projects in other parts of Guyana, the Guiana Shield and Amazon countries. 80 Appendix 2 -­‐ Quiz for PMT & CREW Workshop -­‐ Sunday 26th May Technology 1. How well do you feel you can use the mobile device? Can’t use it □ Not so well □ Quite Well □ Really Well □ Outstandingly Well □ 2. What are the main problems you have with using the handheld? ..................................................................................................................................... ..................................................................................................................................... Climate Change and REDD+ 1. In your own words, what does REDD+ mean? ..................................................................................................................................... ..................................................................................................................................... 2. What does climate change mean? ..................................................................................................................................... ..................................................................................................................................... The Project Objectives 1. Can you state here what the main aims of the cMRV project are? Name as many as you can: 1. ...................................................................................................................................... 2. ...................................................................................................................................... 3. ..................................................................................................................................... 4. ..................................................................................................................................... 5. ..................................................................................................................................... 6. ..................................................................................................................................... 2. From the aims you stated, which ones do you feel have been achieved? (for example 1 & 2) ..................................................................................................................................... Impact of the Project 1. What do you feel has been the main impact of the cMRV project? (The impact can be positive or negative. It can be an impact to you, your community or your surroundings) ...................................................................................................................................... ...................................................................................................................................... ...................................................................................................................................... ...................................................................................................................................... ...................................................................................................................................... ...................................................................................................................................... 81 Appendix 3 – Community Questionnaire Community)Social)Network)Questionnaire)
!
This!questionnaire!will!ask!questions!which!may!be!quite!personal!about!who!you!communicate!
with!in!your!village.!If!you!wish!not!to!answer!a!question!please!leave!it!blank.!All!the!information!
given!by!yourself!will!only!be!seen!by!myself!(Charlotte!Selvey,!of!Imperial!College!London).!Your!
name! will! not! be! used! in! any! reports! and! all! your! information! will! remain! completely!
anonymous.! The! report! will! be! made! fully! available! upon! completion! and! you! will! receive! full!
access!to!this.!!
!
!
1.)Name:)
Sex:)
)
2.)Village:)
Age:))Under!18☐!!18G29☐!!30G49☐!!!50G69☐!!70+☐!
)
3.)How)many)people)are)in)your)household?))
)
)
4.)Can)you)list)the)5)people)you)communicate)with)most)(not)including)your)immediate)
family(parents,)brothers/sisters,)children))
!
1.!
2.!
3.!
4.!
5.!
!
!
5a.)How)many)people)in)the)village)obey)the)village)rules?))
None!☐ !!!!!!!!!Only!some!!☐!!!!!!!!!!!!!!Half!!☐!!!!!!!!!!!!!!!Most!people!☐
Everyone ☐!
!
!
5b.)Who)are)the)people)you)trust)most)in)your)village)(not)including)your)immediate)
family)?)Name)top)3.!
1.!
2.!
3.!
!
!
6.)Who)are)the)people)you)look)up)to)and)respect)most)in)the)village)(apart)from)your)
immediate)family)?)Name)top)5.!
1.!
2.!
3.!
4.!
5.!
)
)
)
7.)How)often)do)you)speak)to)……………………….)&)………………………...?))
)
Daily☐!!!Weekly☐!!!Monthly☐!!!Once!or!Twice!a!year☐ Less then once a year☐
Never☐!
If you tick ‘never’ then skip Q.8!
!
)
)
8.)What)are)the)reasons)you)speak)with)……………………..)&)………………………?)
82 !
………………………..:!!!!!!!!!!!!!!Social☐!!!!!!!Giving!advice!to!them☐!!!!!!Taking!advice!from!them☐!!!!
!
Business☐!!!!!!!!!!!!!!!!!!!!!!!!!Family☐
cMRV☐
Other ☐ Please Specify ‘other’:!
!
!
………………….…….:!!!!!!!!!!!!!Social☐!!!!!Giving!advice!to!them☐!!!!!!!!Taking!advice!from!them☐!!!!!!!!!!!!!!!
!
Business!☐!!!!!!!!!!!!!!!!!!!!!!!Family☐
cMRV☐
Other ☐ Please Specify ‘other’:
!
!
9.)What)do)they)do?)(job))
!
Please state: !
!
)
10.)Do)you)own)a)mobile)phone?)
)
Yes!!☐!!!!!No!!☐!
)
11.)Do)you)have)an)email)account?)If)so,)how)often)do)you)access)it?)
)
I!don’t!have!an!email!account!
☐!
I!have!an!email!account!but!never!check!it!
☐!
I!check!my!email!every!month!
☐!
I!check!my!email!every!week!
☐!
I!check!my!email!every!day!
☐!
)
)
12a.)What)does)REDD)stand)for)and)mean?))
Please!explain!what!REDD!means:!!
)
)
12b.)What)do)you)know)about)climate)change)
Please!explain:!!
)
)
)
)
13.)Have)you)heard)of)cMRV?)If)no)Z)end$of$questionnaire.)
)
Yes!!☐!!!!!No!!☐
)
83 14.)Can)you)explain)what)it)is?))If)yes)please)explain)briefly)in)box.
)
Yes!!☐!!!!!No!!☐!
!
)
)
15.)Have)you)seen/heard)of)your)community)CMRV)reports?)If)so,)what)are)your)thoughts?)
!
!
Yes!!☐!!!!!No!!☐!
!
)
16.)How)you)would)improve)the)CMRV)report?)
!
!
!
17.)In)your)opinion,)thinking)back)over)the)past)year,)has)anything)significantly)changed)
for)you)or)anyone)around)you)as)a)result)of)the)cMRV)project?)(e.g.)changes)to)your)life,)
your)surroundings,)your)community,)the)region,)people’s)understanding)etc).)The)change)
can)be)positive)or)negative.)(Reminder)that)this)will)remain)anonymous.))
!
Yes!!☐!!!!!No!!☐ Don’t!know!☐
Please!explain!what!those!changes!are:!!
)
18.)Please)write)down)any)other)comments)you)want)to)make)about)the)CMRV)project.)
Your)comments)are)greatly)appreciated)and)they)will)be)heard)to)make)sure)
improvements)can)be)made)to)the)cMRV)project.)
!
)
Thank$you$for$completing$this$questionnaire.$You$will$receive$information$about$this$
research$as$soon$as$it$is$completed.$
84 Appendix 4 – Social Network Questionnaire Social Network Questionnaire for cMRV project members This questionnaire is part of a wider effort to better understand the dynamics of the community MRV project and to hear your thoughts. Findings from this questionnaire will be kept confidential and all information will be kept anonymous. Only Charlotte Selvey, of Imperial College London will see individual questionnaires. Your answers in no way will alter how you are treated as a participant and/or employee of the cMRV project. This is a voluntary survey and if you do not feel comfortable answering any question you may leave it blank. Questionnaire results will be reported back to the cMRV team and partners and a summary will be available to you if requested after completion. Results will not identify individual partners or employees. 1. Background Information Name: Please name the organisation you work for or represent: How many years have you been involved with cMRV? What is your main occupation? 2. Social Network Please list the people who you know best who participate in the cMRV project. How often do you communicate (phone, email, letter, face to face, etc.) Daily 1. 2. 3. 4. 5. Weekly Monthly Yearly 85 Please fill in this table as best you can thinking about the time since cMRV started. Please put a cross through your own name. cMRV Partner How often you communicat
e (daily/weekl
y/ never etc) How do you communicat
e (phone/email
/letter/face-­‐
to-­‐face/all) Reasons for communica
tion (e.g social/ business/ family/ advice) Do you wish to communicate with this person more or less or the same? Strength of your relationship (+5 like a lot, -­‐5 dislike a lot) 0 = Neutral X= I don’t know them Raquel Thomas-­‐
Caesar Vandar Radzik Cindy Lawrence Colin Simon Courtney Peters Delon Abraham Deroy Roxroy Bollers Derick Low Luis Meneses Mary Menton Jon Parsons Ben Palmer-­‐Fry Tjeert Wits Claudia Comberti Han Overman Bryan Allicock Vitus Antone Vivan Moses Althea Hamilton Shurland Davis Livingstone Hamilton Michael Williams Ivor Marslow Emily Allicock Rosie George-­‐
Alam Allan Roberts Arnold Bartholomew Campbell James Caroline Jacobs Augustine Edghil Bowen Francis Roberts 86 3. Collaboration and communication in cMRV Lincoln Francis Marcus Moses Nadia Norman Nyola Edwards Rebecca Francis Rosita Roberts Samantha Garner Edwards Glenda Joseph Grifley Mack Handley Thomas Harvey Jacobus John Benjamin Julian Bresche Norbert Salty Kenrick Campbell Kenrick Benjamin Kurt Singh Leonie Ewell Andries Susan George Tom Henry James Roberts Mallory Moses Ernest Merriman Travis Franklin Keith Sutherland Neil Ignacio Please list below any other individuals you believe are involved and play an important role in cMRV which have not been stated above. 1. 2. 3. Verley Jacobus 87 Please list the 5 people you trust the most from the list above. (1 = most trust worthy) 1. 2. 3. 4. 5. Which 5 people do you consider the most successful from the list of everyone included in cMRV? 1. 2. 3. 4. 5. Please indicate the definition of success you have used (ie, business, happiness, income etc): Can you rank in order the ways in which you get information about cMRV? This information can be new ideas or information that has been passed on. Please allocate 1-­‐4 next to each way of getting information (1= most used, 4= least used). a. Workshops and Meetings b. Written reports c. Informal personal communication d. websites Please think about the 5 people who you interact with the most socially, but who don't live in the same house as you. They could be friends or family. Please list their names in order of how well you feel that you know them. (1= you know the best) 1. 2. 3. 4. 5. End of Questionnaire. Thank you for taking the time to complete this 88 Appendix 5 – Letter of Permission !
!
North Rupununi District Development Board
Bina Hill
Annai Village
North Rupununi
Region # 9
2013-05-21
The Toshao/Senior Councillor
………………………….Village.
Dear Toshao/Senior Councillor
REF: CMRV-GCP PERSONNEL WORKING IN YOUR VILLAGE.
With reference to the above mentioned subject I Bryan Allicock Project Manager of
Community Monitoring, Reporting and Verification project kindly seek your approval to
have these three persons do some work on behalf of the project for at least two days
and one night. They are doing an exercise in assessment on how has the project made
an impact or assisted in your village development or how you could use the information/
data gathered from your village continue your village development.
Their Names are:Claudia Comberti
Charlotte Selvey
Ben Palmer-Fry
They will be in Apoteri on the 28th-29th May 2013
Fairview on 30th -31st May 2013
Katoka on 1st -2nd June 2013
Kwatamang on 4th -5th June 2013
Annai Central on 8th -9th June 2013
!
!
!
89 !
!
Rupertee on 6th -7th June 2013
Surama on 10th -11th June 2013
Would be very grateful if my request could be considered.
Thanking you very much for your usual cooperation.
Yours Cooperatively
……………………………..
Bryan Allicock
Project Manager
C-MRV
!
!
!
90