Human-induced land use and land cover changes (LULCC) are a major component of global environmental change (Turner et al. 2007, Ellis 2015), with profound consequences for the climate system through land surface feedbacks (IPCC 2007, Ostberg et al. 2015), biodiversity (Barnosky et al. 2011), and human welfare and development (Griggs et al. 2014). Assessing possible future LULCC is a fundamental need if we are to embed sustainability in development strategies, ecosystem management, and land use planning, particularly for developing countries where rates of LULCC are highest (Rounsevell et al. 2012, Verburg et al. 2013).
The complexity of LULCC results from interactions across biophysical, socioeconomic, and governance factors occurring at different scales (Parker et al. 2008, Meyfroidt et al. 2014) and it is increasingly explored and interpreted through the lens of coupled human-natural systems (Binder et al. 2013, Liu et al. 2015). Within these frameworks, scenario analysis has been widely applied to explore future trajectories, at different scales and using different approaches (Alcamo 2008, Amer et al. 2013, Oteros-Rozas et al. 2015). Through a scenarios approach, uncertainty and complexity can be tackled across multiple thematic dimensions (Peterson et al. 2003, Mahmoud et al. 2009), integrating explorative pathways with normative visions that identify a diversity of potential, as well as desired, futures states (Rounsevell et al. 2012) that engage multiple stakeholders (Johnson et al. 2012, Reed et al. 2013).
At a national level, governments elaborate visions for the future that often underpin policies via national development plans and commitments into international mechanisms, such as the Sustainable Development Goals (UNDESA 2015), the United Nations Aichi Targets linked to the Convention on Biological Diversity (UNEP 2010), the United Nations Framework Convention on Climate Change (UNFCCC), and the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES; Zisenis 2014). Global or large-scale scenario analyses (e.g., IPCC 2000, UNEP 2012, van Vuuren et al. 2015) have typically been conducted through top-down, expert-driven processes, and are only weakly connected with grassroots initiatives (Leach et al. 2012). These approaches are inappropriate for assessing national policies, for example on land use and sustainable development, against local impacts and locally tailored solutions that are not captured in larger scale narratives.
Leach et al. (2012) emphasized the need to reconnect top-down policy mechanisms with grassroots innovation and knowledge, to identify new pathways toward sustainability from the bottom upward. Such an approach implies enhanced participation of stakeholders, i.e., those who are affected by or can affect a decision or action (Freeman 1984) in the process. Indeed, stakeholder participation is a fundamental prerequisite of sustainable development, particularly for understanding the multidimensional interactions between societal and environmental challenges, both in the policy (UNCED 1992, UN 2012) and in the modeling frameworks (Fraser et al. 2006, Reidsma et al. 2011, Grêt-Regamey et al. 2013, Danielsen et al. 2014, Herrmann et al. 2014). The importance of stakeholder engagement has gradually evolved from respect for peoples’ right to participate in decision making, to a means of enhancing the sense of ownership, relevance, and legitimacy of the process (Bell et al. 2012, Priess and Hauck 2014), the understanding of its outputs (Sohl and Claggett 2013), the promotion of social learning (Johnson et al. 2012, Castella et al. 2014), and thus the chances of successful implementation of associated policies and interventions (Reed et al. 2013).
A number of challenges remain for integrating stakeholder participation with scenario analyses and quantitative modeling to support decision making at subnational and national scales (Rounsevell et al. 2012, Sohl and Claggett 2013, Verburg et al. 2013, Castella et al. 2014). First, new methods are required to codify varied and sometimes conflicting knowledge held by stakeholders, to inform how different drivers of change may play out in the future scenarios (Reed et al. 2013). Second, there is the challenge of transforming qualitative storylines into sets of coherent quantitative information within the participatory process (Walz et al. 2007), and to use that information in customized spatial models (Swetnam et al. 2011). Toward these ends, recent studies have proposed new approaches that enhance the role of stakeholders in scenario analysis, especially for assessing and modeling future LULCC and their possible impacts (Swetnam et al. 2011, Lamarque et al. 2013, Malinga et al. 2013, Hanspach et al. 2014, Rosenberg et al. 2014). However, such integrated approaches have, so far, been restricted to subnational scales or single units of analysis, with limited scope for synthesis (Stringer et al. 2006) or upscaling to the national level.
In response to these challenges, we present a methodological framework for participatory development of spatially explicit, integrated socioeconomic and environmental scenarios that reconcile subnational perspectives across a nation. With this framework, we aim to: (1) strengthen the participation of stakeholders in linking socioeconomic trajectories to LULCC; (2) disentangle the complexity of socioeconomic drivers and their causal relations with LULCC; (3) capture and reconcile subnational perspectives at the national scale; and (4) ease the transfer of knowledge to inform decision making at national and subnational scales. We outline our application of the framework to envision future trajectories of LULCC and habitat degradation across mainland Tanzania. We follow with an evaluation of the framework in relation to Tanzania and beyond, and conclude with a discussion on the possible contribution of such a stakeholder-driven approach for elaborating new, achievable sustainable development pathways.
The proposed scenario framework consists of four main steps that involve experts (facilitators and modelers) and stakeholders (those who are affected by or can affect socioeconomic and land dynamics), and guides them to develop scenarios with qualitative, quantitative, and spatially explicit elements (Fig. 1). Scenarios are developed independently at subnational level before being synthetized at national level through a mixed stakeholder-driven and model-based approach.
In the first step, the focus of the analysis is identified, either by a panel of experts or by a broader group of stakeholders, and key boundary conditions are set including the spatial units of analysis at subnational level, e.g., administrative or management units, base year, and time horizon. Initial and potential future desired or normative conditions are purposely presented as general and synthetic statements to allow stakeholders to develop locally oriented storylines. Scenarios are set while keeping in mind that, in participatory processes, people are able to process a limited number of alternative scenarios at a time, e.g., four or fewer (Reed et al. 2013). Stakeholders are identified (Luyet et al. 2012) to represent relevant segments of the society at subnational (for both subnational- and national-level workshops) and national level (for the national-level workshop only).
For each subnational unit of analysis, subnational scenarios are developed during multistakeholder workshops. This is achieved through two main tasks (Fig. A1.1a). First, all participants select the economic sectors most relevant to local livelihoods and land use. In parallel focus groups participants discuss the factors driving current situations, envisage alternative futures under scenario conditions, and position the sectors on charts representing economic and environmental axes (from “poor” to “rich” and from “degraded” to “healthy,” respectively). These charts are used to exemplify trade-offs between socioeconomic, i.e., income, production, and livelihood, and environmental, i.e., LULCC and resource depletion, interrelationships in sectoral trajectories (Fig. A1.1b). Sectoral trajectories may cut across quadrants, following participants’ visions of what environmental and economic changes are likely in their region.
In the second task, participants’ groups link the socioeconomic trajectories identified for the future scenarios to specific and spatially defined LULCC, using a reference land use and land cover map (Fig. A1.1c). For each conversion from one land-use-cover class to another (Fig. A1.1d) participants evaluate its likelihood on a scale ranging from 0 (“not possible”) to 4 (“very likely”). They rank the specific drivers by their relative importance and report where changes would likely occur in the landscape. Participants are encouraged to report spatial information such as specific sites of potential LULCC, e.g., administrative units or gazetted sites, or biophysical factors associated with them, e.g., “near roads” or “in fertile soils”).
For both tasks, participants work in mixed groups across administrative units and sectors to generate consensus and harmonize visions within each subnational unit of analysis. Qualitative descriptions of trajectories, including drivers by sector and scenario, and quantitative LULCC assessments are recorded on open-ended formatted forms by the group, not by individual. Outputs are then compared across groups in plenary sessions where stakeholders discuss their different perspectives until a consensus is negotiated. During the workshops, facilitators take notes and ensure a collective understanding of the objectives (Sandker et al. 2010), but they aim to do so without actively participating in the discussion.
The modeling step follows the completion of participatory workshops in every subnational unit. First (Step 3a), subnational workshops outputs are checked, compared, and integrated across groups. This analysis produces intermediate outputs that enhance the interpretation of the final scenario outputs, i.e., national scale LULCC quantification and mapping, providing additional information for decision making and spatial planning. From Task 1 of Step 2, qualitative outputs are integrated and codified focusing on drivers of the future scenarios, and spatial distribution of LULCC. Sector-specific trajectories from charts are translated into numerical vectors that can be used to distribute land demand across subnational units. From Task 2, LULCC likelihood scores are cross-tabulated to compare potential losses and gains for each land-use-cover class, and corresponding drivers and spatial information are identified. The relative importance of drivers is assessed based on frequency, likelihood of change score, and relative ranking.
Second (Step 3b), global and national spatial datasets are selected to represent the spatial information associated with LULCC at subnational and national scales, and are used as single dimensions of spatial composite indicators (CIs) of LULCC likelihood. The CIs can synthetize complex information into a scalar quantity, which can be compared across analysis units and then easily communicated to a nonexpert audience (Saisana and Tarantola 2002). The single dimensions, i.e., spatial datasets, of CIs are tested for collinearity to avoid redundancy and, where necessary, are reduced, taking into account workshop participants’ statements. The spatial datasets representing the selected dimensions are reclassified to a common scale following the spatial patterns described by participants, then combined by linear aggregation, and finally multiplied by constraining factors to account for areas where changes are limited or excluded (Fig. A1.1). Composite indicators of LULCC likelihood are created for every LULCC type in each analysis unit. Finally, CIs are rescaled to a common scale (from 1 to 10) using maximum-minimum method and merged at national scale (Fig. A1.1).
In Step 3c, land demand under the future scenarios is estimated from available data and literature at the national and local level, and according to the trajectories developed by stakeholders. In Step 3d, demand is allocated across subnational units and land-use-cover classes following (1) the relative impacts of the economic sectors assessed by stakeholders, (2) the relative share of land-use-cover classes, (3) the specific likelihood scores associated with each LULCC type, and (4) the specific CIs of LULCC likelihood. Pixels are converted until land demand is fulfilled (Fig. A2.4).
In this step, subnational scenarios and a preliminary quantitative national synthesis are presented in a national-level workshop involving stakeholders representing both the subnational units and the national level. Through the revision of inputs (data, assumptions) and outputs (scenarios trajectories and maps) of the scenario analysis, the workshop aims to receive feedback on the process, to identify and fill in possible gaps, and to reconcile competing perspectives between subnational perspectives and national level harmonization. This is followed by a revision of the modeling step and the same feedback-oriented process, iterating the cycle as required until consensus is reached.
We applied this step-wise scenario framework across mainland Tanzania (~ 883,600 km²; Fig. 2) within the context of a national readiness initiative for the program Reduced Emissions from Deforestation and Degradation (REDD+). Tanzania’s National Development Vision 2025 sets development goals to turn the country into a middle-income economy by 2025 (URT 2005), and has inspired efforts toward sustainable development in conformity with the United Nations Sustainable Development Goals (SDGs). Tanzania’s mainland population reached 43.6 mil in 2012 (2.7% annual growth rate since 2002), with the majority (70.9%) inhabiting rural areas and reliant on a semisubsistence economy (NBS and OCGS 2013, 2014). The country’s annual GDP growth rate averaged 7% between 2002 and 2014 (World Bank 2014); the headcount ratio for the Multidimensional Poverty Index was 65.6% of the population in 2010 (Alkire and Robles 2015). A diversity of ecosystems (Burgess et al. 2004) provide fundamental services both for local livelihood, e.g., water and climate regulation, soil protection, timber and wood fuel provision, and grazing land, and for the national economy, e.g., hydro-power for energy production or nature-based tourism (Fisher et al. 2011, Willcock et al. 2016). Globally important biodiversity hotspots (Burgess and Clarke 2000, Myers et al. 2000, Burgess et al. 2007), and core areas for key populations of large mammals (Brooks et al. 2001) are included in a large network of reserved areas with different protection designations, covering almost one third of mainland Tanzania (IUCN and UNEP-WCMC 2015). These areas are facing various pressures from human activities, e.g., encroachment, illegal timber harvesting, or mineral extraction (Lange 2008, Pfeifer et al. 2013, URT 2014). In the unreserved land, ~ 44 million ha is considered potentially available for agricultural expansion (URT 2014), attracting the interest of large-scale investors, e.g., the Southern Agricultural Growth Corridor of Tanzania (SAGCOT). Between 1995 and 2010 the deforestation rate over the country has been estimated at 100,000 to 400,000 hectares per annum (MNRT 2015, Willcock et al. 2016).
Like other developing countries, Tanzania has endorsed payment for ecosystem services (PES) schemes to support sustainable development pathways of local communities, among which is the REDD+ program (URT 2013a), building on existing community-based natural resource management initiatives (Burgess et al. 2010, URT 2013b). Although REDD+ has undergone much critical exploration (e.g., Chhatre et al. 2012, Mustalahti et al. 2012) implementing it could trigger a shift toward an economic model that stimulates sustainable resource use and decreased LULCC rates, generating a positive cascade on livelihoods (UNEP 2015). In the application of our scenario framework to Tanzania, we aimed to assess the potential for such a development model and the contribution of PES schemes to its attainment, using a “green economy” scenario (GE), as an alternative to the current development trends, which we refer to as “business as usual” (BAU). Scenarios boundary conditions (Table 1) built on the study conducted by Swetnam et al. (2011) in eastern Tanzania, combined with a literature review (e.g., URT 2005, 2011, NBS-OCGS 2013, URT-MASFC 2013, World Bank 2014). Under BAU, current trends in governance, population growth, deforestation, degradation, and cultivated land expansion continue. The GE scenario includes the normative target of implementing REDD+ and other PES schemes, but it is also partially explorative, i.e., roadmap to be established, of pathways toward sustainable development focused on trade-offs between cultivated land expansion and forest management. The base year was set to 2010, consistent with the baseline reference land use and land cover map (MNRT 2013), while the time horizon was set to 2025, in agreement with Tanzania’s National Development Vision (URT 2005) and the SDG timeline.
At subnational scale, our analysis units were the seven management zones of the Tanzania Forest Service (TFS; Fig. 3). Between February and June 2014, seven back-to-back multistakeholder workshops were conducted, each lasting two days and involving 180 participants in total (Table A1.1; WWF-TCO 2015). Stakeholder identification and selection for inclusion in the participatory processes (Steps 2 and 4) followed the criteria of representativeness, knowledge at the scale of the analysis, and skills for participating in the process. We invited governmental institutions, private companies, research institutions, and civil society organizations (CSOs) representing land users, land managers (technical and political) at municipal, district, and regional level, with expertise in socioeconomic and development sectors. Local (village-level) communities were represented by farmers and livestock keepers associations, community-based natural resources management and conservation organizations, and women’s groups. Participants were asked to complete anonymous questionnaires at the end of the workshops to provide feedback on the process. The synthesis workshop was conducted in October 2014 and gathered 60 stakeholders from public institutions (mainly at national level), research institutions, CSOs, agribusiness, and media (Table A1.b; WWF-TCO 2015).
Further details on the framework application in Tanzania are included in Appendix 1 for stakeholder-driven steps and Appendix 2 for LULCC and demand modeling steps. Given uncertainties on cultivated land surface at the base year (Appendix 2), we simulated two BAU scenario patterns: BAU1 (expansion of intensively cultivated land only) and BAU2 (additional expansion of mixed cultivated-wooded land). All spatial and quantitative analyses were performed in ArcGIS 10.2 (ESRI 2014) and R (R Core Team 2014).
In the BAU scenario, with the population reaching ~ 62 million by 2025 in the Tanzania mainland and with no productivity gain, cultivated land expands at a rate of ~ 2% per year. Simulated LULCC amounts to 53,867 km² of new cropland and, under BAU2, 34,941 km² of additional mixed cultivated-wooded land by 2025, mainly through the conversion of woodland (Table 2). Estimated wood demand of 1.3 m³/capita/year is not entirely fulfilled by conversion to cultivated land and leads to additional habitat degradation, i.e., loss of tree cover and biomass without replacement from cropland, over 80,427 km² (BAU1) or 33,047 km² (BAU2) of woodland, bushland, and forest (Table 2).
Under the GE scenario, assuming the same population growth as in BAU, a 10% increase of crop productivity and no further expansion of mixed cultivated-wooded areas, cultivated land expands by 44,132 km² (Table 2) and conversion of natural forest and closed woodland is reduced compared to the BAU scenario. In this scenario, assuming a 50% reduction of demand exceeding sustainable annual harvest, an additional 35,778 km² of woodland and bushland are degraded (Table 2).
Subnational workshop participants reported that spatial patterns of habitat degradation are determined by factors such as proximity to human settlements and roads, but also mismanagement of resources in specific sites, e.g., protected area borders and forest reserves (Fig. 3). Cultivated land is most likely to expand near human settlements, roads, and irrigated sites (Fig. 3). The likelihood of both potential habitat degradation (as a consequence of wood extraction) and of cultivated land expansion is the highest in the northern (Tanga) and southern (SAGCOT) development corridors (Fig. 3). In the BAU scenario (both BAU1 and BAU2), cultivated land expansion rates are highest in the Southern Zone, but the largest conversion is in the Central Zone (Fig. 4), where it was envisaged that lower productivity due to poor agricultural practices would lead to high rates of land conversion. Under the GE scenario, cultivated land expansion rates are highest in the Eastern and Southern Highlands Zones, and degradation (both rates and area) is highest in the Southern Zone (Fig. 4).
Subnational workshop participants developed storylines composed of qualitative, quantitative, and spatially explicit elements that characterize the scenarios in each zone, synthesized at national level in Fig. 5 and Fig. 6 for BAU and GE scenarios, respectively. Under the BAU scenario stakeholders emphasized population growth, poor governance, inadequate land use planning, lack of know-how, and poor practices in productive activities, low access to alternative energy sources and income generating activities as underlying factors driving sector trajectories (Fig. 5a). Participants generally envisaged economic growth at the expense of the natural environment, but negative economic trends were expected for agriculture, livestock, energy, and mining sectors in the Central Zone and for agriculture in the Lake Zone (Fig. 5b). Participants suggested that economic sector trajectories would be interdependent at individual or community level, e.g., charcoal production as alternative income generation activity to farming during dry season, and that they could be affected by cultural factors. Under the BAU scenario (Fig. 5c), among the direct drivers of LULCC, population growth was perceived to have the highest impact in the Northern Zone, farmland expansion in the Southern Zone, wood fuel production in the Western Zone, livestock keeping in the Central Zone, timber forest product extraction in the Southern Highlands, and human-set fires in the Eastern Zone.
For the GE scenario, participants reported technical improvements, law enforcement, land use planning, and good practices, e.g., in land use and economic activities management to be among the main opportunities for green development (Fig. 6a), leading to reductions in environmental impact and improvements in livelihood (Fig. 6b). However, under this scenario, trajectories did not always cross onto the positive side of the environmental axis, i.e., “healthy environment,” suggesting that participants were not expecting to reach a high level of environmental sustainability within the scenario time frame (Fig. A1.1). In the Eastern Zone participants did not envisage any GE scenario for the livestock sector (Fig. 6b). Among the direct drivers of LULCC reduction in the GE scenario (Fig. 6c), land management, e.g., planning areas for human settlement and cultivated area expansion in respect of sustainable forest management, was perceived most important in the Northern Zone, law enforcement and governance in the Lake Zone, e.g., in reference to participatory forest management, conservation in the Southern Highlands Zone, forest management in the Eastern Zone, financial incentives in the Southern Zone, and afforestation in the Lake Zone.
The proposed scenario framework enhances the role of stakeholders in scenarios development, particularly in (1) envisioning future socioeconomic-environmental trajectories and (2) quantifying their impacts as specific LULCC. In our application in Tanzania, one or both objectives were reported as challenging by most individual participants at subnational workshops (82% of 125 respondents to feedback questionnaires), particularly the LULCC analysis (70%). Nonetheless all focus groups were able to complete the assigned tasks. Facilitators faced the challenges of eliciting participation of group members and collaboration within the group without imposing any personal bias, and of objectively guiding participants to converge from comprehensive discussions to specific impacts. Participants generally reported increased understanding of landscape dynamics following the workshop, suggesting a potential for capacity building despite the technical complexity (Johnson et al. 2012, Oteros-Rozas et al. 2015). Overall engagement and understanding of the participatory tasks proved similarly high across the seven zones in Tanzania, though different communication tools and timing may be considered when applying the framework at a different scale, depending on education and experience of the participants (Reed et al. 2013, Butler and Adamowski 2015).
This framework does not limit the type of information stakeholders can provide on the spatial patterns of LULCC, nor are there compulsory indicators to be used in the modeling step. Instead, there is a targeted effort to obtain and process the relevant spatial datasets after the workshops. In this way, it is possible to capture perspectives on factors other than biophysical or economic properties, e.g., “LULCC will happen where people need land or where there is corruption,” as opposed to “LULCC will happen at forest edges or where land rent is high.” Although this approach may increase the complexity of the modeling steps, it ensures that stakeholders are free to express any information they deem to be important. In cases where the required spatial datasets prove to be unavailable or of poor quality, this can guide future efforts to fill such gaps in knowledge, while in the meantime focusing on the most relevant indicators for which data are available.
Important advances of this framework beyond the related methodology of Swetnam et al. (2011) include the identification and quantification of region-specific patterns of causality behind LULCC, and the differentiation of processes and extent of habitat degradation versus whole-scale conversion. The resulting composite indicators and LULCC likelihood maps facilitate communication of scenario outputs to decision makers in a way that explicitly accounts for uncertainty, e.g., “likelihood of LULCC equal to 4 on a 1-10 scale,” and captures either overlaps or spatial segregation of different LULCC pressures that assists in the planning of spatially distinct actions (Riedler et al. 2015). Even though the proposed framework has a clear directional flow toward the defined alternatives, it allows us to explore how different factors and drivers can contribute to a range of alternative pathways, and investigate less likely options. Competing perspectives are also easily identified and may be used to generate focus when transferring lessons learned from scenarios to decision makers (Castella et al. 2014).
The framework aims to ensure consistent applications to multiple areas. By capturing stakeholders’ perspectives through explicit and standardized means, e.g., likelihood scores, it permits reproducibility across subnational units while maintaining representation of local subjective perspectives in the upscaling process (Stringer et al. 2006). Shared visions at subnational level were negotiated within each unit of analysis by workshop participants, and often proved consistent across the zones. Reconciling visions between subnational and national governance levels, however, was sometimes challenging because of the different roles covered by stakeholders (implementers versus decision makers). For example, subnational and national level stakeholders expressed different levels of confidence regarding the success of existing policies being implemented effectively. When interpreting scenario outputs we consulted secondary information to validate one or other stakeholder perspectives. Such divergences were pointed out in the synthesis workshop, and were further discussed in the iteration step.
In capturing subnational and national perspectives in Tanzania, we also faced challenges in terms of representativeness and replicability (Oteros-Rozas et al. 2015). Participant selection for stakeholder workshops was made by organization rather than by invitation of specific individuals. This limited our control over participants’ characteristics, and may have reduced the range of voices heard in the construction of our scenarios (Luyet et al. 2012, Butler and Adamowski 2015). One example was the low level of attendance by women, especially within governmental organizations, and in particular at national compared with subnational levels. Women are reported to have limited opportunities in the public sector in Tanzania (Strachan 2015), and tend to be excluded from official land use decision making or planning processes in other developing countries (World Bank 2008, Bourgoin et al. 2012). Furthermore, we could not ensure repeat attendance of all stakeholders at both subnational- and national-level workshops (Reed et al. 2013).
Despite our effort to design a stakeholder-driven process, experts’ facilitation and modeling skills were still required to generate and communicate the final outputs. In addition to the logistical cost of the participatory step, this commitment in time and resources limited the number of scenarios we could develop, as well as feedback opportunities with stakeholders. Local resources should increasingly be employed to abate the implementation cost. Investing in the capacity and feasibility of (more) autonomous application of tools such as this framework at local scale, and at lower cost, is then a critical challenge for enhancing bottom-up engagement in sustainable development processes (Tschakert and Dietrich 2010), along with improving accessibility to data and decentralization of information sources, and developing platforms for continuous feedback exchange among stakeholders.
In our application, we presented a green economy alternative to the business as usual, to stimulate discussions and emphasize contrasts in the final outcomes (Carpenter et al. 2015). This facilitates understanding of the scenario concept for those who are not accustomed to it, but may give the impression that there is just one comprehensive alternative to the business as usual. Participants in the process did not develop purely bad or purely good alternative scenarios, as could be to some extent suggested by the initial definitions, and they carefully evaluated possible trajectories. However, they pointed out that the real future could be a mix of the two scenarios. In the proposed framework, the disaggregated analyses of economic-environmental trade-offs contributes to an understanding of competition or synergies among different drivers and policy objectives, and so provides a starting point for hybrid scenario analyses. Policy trade-offs should be addressed directly in further scenario exercises to ensure their relevance in policy debates and buy-in of decision makers.
Our scenarios outputs represent two plausible interpretations of the many possible divergent futures for Tanzania. The presented LULCC quantification is limited to some of the most relevant economic sectors discussed during the workshops. We deem our scenario assumptions valid within the 2025 time frame, while in the longer term other emerging processes could significantly affect socioeconomic and environmental trajectories, in particular natural gas and oil extraction, rural-to-urban migration, introduction of PES schemes, IT development, climate change, and capacity building. Our outputs should be interpreted jointly as an expression of a large, though limited, number of stakeholders, at the time (2014) and at the scale (macro-regions) of the workshops, and should be used along with, and not in replacement of, other analytical approaches, particularly those that harness representation at local scales (e.g., Enfors et al. 2008, Tschakert and Dietrich 2010, Brammer et al. 2016).
When considering the envisaged trends in the BAU scenario, Tanzania seems unlikely to achieve its National Development Vision goals by 2025. This would require high growth and structural transformation sustained by large productivity gains (Moyo et al. 2012). In the BAU scenario, lack of improvement in productivity and agricultural practices is expected to affect local food security in the next decades (MAFAP 2013, URT-MAFSC 2013) and/or induce vast LULCC, with commensurate impacts on water and climate regulation, biodiversity (Green et al. 2013, Kideghesho et al. 2013, Caro and Davenport 2015) and livelihoods (URT 2011). Expansion of large-scale international commercial farming may play a critical role in the next decade (Rulli et al. 2013, Laurance et al. 2015). A review of investment policies in Tanzania (OECD 2013) largely confirmed the regional stakeholders’ vision that land tenure insecurity and a heavy bureaucratic burden have discouraged foreigner investors to date, and thus slowed the implementation of development corridors championed at national scale. The Southern Agriculture Growth Corridor of Tanzania (SAGCOT, a public-private partnership) was considered by regional stakeholders either as an opportunity for boosting the agriculture sector, e.g., in the Southern Zone, or as a risk if benefits do not reach the local communities but remain with international corporations, e.g., Eastern and Southern Highland Zone. National stakeholders considered SAGCOT part of the GE scenario (Milder et al. 2013), though they warned that “the impacts could be different than expected.”
Farmland expansion and charcoal production are often associated LULCC drivers, though causality relations between them vary across Tanzania. As a consequence, in the GE scenario productivity gains in the agriculture sector contribute to reduced habitat degradation along with the implementation of more efficient and sustainable fuel production, the creation of alternative employment, and the acknowledgment of political responsibilities in mismanagement of local forest resources (Burgess et al. 2010, Sander et al. 2013). For this scenario, PES schemes were expected to support changes in the development pathway by eliciting policies enforcement, e.g., on sustainable forest management, conservation, and reforestation, and integration, e.g., between poverty reduction and environmental policies, and to a lesser extent by direct benefit of financial incentives.
Stakeholders expected that emerging mining and infrastructure sectors could positively support a green economy if benefit sharing mechanisms and environmental safeguards were in place. Infrastructure development in the near future, e.g., road improvement and rural electrification, could lead to livelihood changes and business development, and in turn to a decreased dependency on natural resources and further development of the tourism sector. On the other hand, increased accessibility, often associated with large-scale agriculture and mining development rather than local demand, could spread degradation and deforestation to currently remote areas (Weng et al. 2013, Jew et al. 2016).
In Tanzania, the complex historical background of land policies has created a dualism between customary and institutional land use rights (USAID 2011). Land rights enforcement and land tenure security would be critical elements for the successful implementation of land use plans, which remain inadequate (URT 2014). In the GE scenario, land use planning was expected to optimize land uses and reduce conflicts among land users. However, this approach may not apply to nomadic communities like pastoralists. The absence of a GE scenario for the livestock sector in the Eastern Zone exemplifies the difficulty of envisaging coexistence between traditional and modern ways of living, and thus of overcoming current conflicts in this region. In the other zones, stakeholders envisaged a cultural change from pastoralism toward modern sedentary ranching, including improved breeds and zero grazing systems, or toward arable farming. These results raise questions on the future of traditional livelihood systems and the associated ecosystems (savannah woodlands) in the country (Hesse and MacGregor 2006) and in the policy debate for alternative development pathways. The absence of discourse between traditional communities and other sections of society, particularly in the explorative GE scenario, is a shortcoming of the framework. Future participatory processes could focus on how the problem of dualism can be addressed in the policy making process and targeting under-represented groups such as pastoralists and other traditional communities. This would require greater direct engagement with those communities, and tuning the spatially oriented approach to capture different perspectives on land uses.
Faced with rapid changes and trade-offs between socioeconomic development goals and environmental sustainability targets, countries such as Tanzania require new frameworks for envisioning and planning desired futures that combine bottom-up perspectives with top-down data sets and policy. In this study, we presented a novel methodological framework for developing scenarios of LULCC through a stakeholder-driven process from subnational to national scale. The proposed framework produces qualitative, quantitative, and spatial outputs that can be jointly used to support ex-ante assessment of development trajectories and policy implementation and of the impacts of consequent LULCC, e.g., on ecosystem services or livelihoods, and to inform decisions for setting spatial priorities for specific interventions. The framework has wide applicability in developing countries, where local communities increasingly participate and create collaborative actions for sustainable management of natural resources and livelihood improvement. However, some important challenges remain:
A greater integration of this framework with local scale scenarios work is a way to pursue these objectives.
Such challenges notwithstanding, the framework proved successful in engaging a wide range of Tanzanian stakeholders in the quantitative assessment of LULCC dynamics. It is the first step towards building a tool that has broad ownership and consensus around future development pathways and policy interventions. The scenario national maps of Tanzania represent the first country-wide, stakeholder-driven assessment of potential socioeconomic and environmental trajectories.
We thank the 180 participants who attended the multistakeholders workshops across Tanzania and who provided the basis of our local stakeholder input to the scenario process. We also benefited from the input of 60 nationally focused decision makers at a workshop in Bagamoyo (Tanzania, 3-4 October 2014) and the insights of the NGO community in Tanzania. We especially thank staff within the WWF Tanzania office, in particular Ms Philippina Shayo and Mr Amani Moshi. We also acknowledge the support of the Norwegian Government through their Royal Embassy in Dar es Salaam who provided the majority of the funding for this work, and the Ministry for Foreign Affairs of Finland, who contributed funding for the analytical work through the CHIESA project.
Alcamo J. 2008. The SAS approach: combining qualitative and quantitative knowledge in environmental scenarios. Chapter 6 in J. Alcamo, editor. Environmental futures: the practice of environmental scenario analysis. Elsevier, Amsterdam, The Netherlands. http://dx.doi.org/10.1016/s1574-101x(08)00406-7
Alkire, S., and G. Robles. 2015. Multidimensional poverty index 2015: brief methodological note and results. Briefing 31. Oxford Poverty and Human Development Initiative, University of Oxford, Oxford, UK.
Amer, M., T. U. Daim, and A. Jetter. 2013. A review of scenario planning. Futures 46:23-40. http://dx.doi.org/10.1016/j.futures.2012.10.003
Barnosky, A. D., N. Matzke, S. Tomiya, G. O. U. Wogan, B. Swartz, T. B. Quental, C. Marshall, J. L. McGuire, E. L. Lindsey, K. C. Maguire, B. Mersey, and E. A. Ferrer. 2011. Has the Earth’s sixth mass extinction already arrived? Nature 471:51-57. http://dx.doi.org/10.1038/nature09678
Bell, S., S. Morse, and R. A. Shah. 2012. Understanding stakeholder participation in research as part of sustainable development. Journal of Environmental Management 101:13-22. http://dx.doi.org/10.1016/j.jenvman.2012.02.004
Binder, C. R., J. Hinkel, P. W. G. Bots, and C. Pahl-Wostl. 2013. Comparison of frameworks for analyzing social-ecological systems. Ecology and Society 18(4):26. http://dx.doi.org/10.5751/ES-05551-180426
Bourgoin, J., J.-C. Castella, D. Pullar, G. Lestrelin, and B. Bouahom. 2012. Toward a land zoning negotiation support platform: “tips and tricks” for participatory land use planning in Laos. Landscape Urban Planning 104:270-278. http://dx.doi.org/doi:10.1016/j.landurbplan.2011.11.008
Brammer, J. R., N. D. Brunet, A. C. Burton, A. Cuerrier, F. Danielsen, K. Dewan, T. M. Herrmann, M. Jackson, R. Kennett, G. Larocque, M. Mulrennan, A. K. Pratihast, M. Saint-Arnaud, C. Scott, and M. M. Humphries. 2016. The role of digital data entry in participatory environmental monitoring. Conservation Biology. http://dx.doi.org/10.1111/cobi.12727
Brooks, T., A. Balmford, N. D. Burgess, J. Fjeldså, L. A. Hansen, J. Moore, C. Rahbek, and P. Williams. 2001. Toward a blueprint for conservation in Africa. Bioscience 51(8):613-624. [online] URL: http://www.bioone.org/doi/abs/10.1641/0006-3568%282001%29051[0613%3ATABFCI]2.0.CO%3B2?journalCode=bisi http://dx.doi.org/10.1641/0006-3568(2001)051[0613:tabfci]2.0.co;2
Burgess, N. D., B. Bahane, T. Clairs, F. Danielsen, S. Dalsgaard, M. Funder, N. Hagelberg, P. Harrison, C. Haule, K. Kabalimu, F. Kilahama, E. Kilawe, S. L. Lewis, J. C. Lovett, G. Lyatuu, A. R. Marshall, C. Meshack, L. Miles, S. A. H. Milledge, P. K. T. Munishi, E. Nashanda, D. Shirima, R. D. Swetnam, S. Willcock, A. Williams, and E. Zahabu. 2010. Getting ready for REDD+ in Tanzania: a case study of progress and challenges. Oryx 44(03):339-351. http://dx.doi.org/10.1017/S0030605310000554
Burgess, N. D., T. Butynski, N. J. Cordeiro, N. H. Doggart, J. Fjeldså, K. M. Howell, F. B. Kilahama, S. P. Loader, J. C. Lovett, B. Mbilinyi, et al. 2007. The biological importance of the Eastern Arc Mountains of Tanzania and Kenya. Biological Conservation 134(2):209-231. http://dx.doi.org/10.1016/j.biocon.2006.08.015
Burgess, N. D., and G. P. Clarke. 2000. Coastal forest of Eastern Africa. IUCN-The World Conservation Union. Gland, Switzerland and Cambridge, UK.
Burgess, N. D., J. D. Hales, E. Underwood, E. Dinerstein, D. Olson, I. Itoua, J. Schipper, T. Ricketts, and K. Newman, editors. 2004. Terrestrial ecoregions of Africa and Madagascar: a conservation assessment. Island Press, Washington, D.C., USA.
Butler, C., and J. Adamowski. 2015. Empowering marginalized communities in water resources management: addressing inequitable practices in participatory model building. Journal of Environmental Management 153:153-62. http://dx.doi.org/10.1016/j.jenvman.2015.02.010
Caro, T., and T. R. B. Davenport. 2015. Wildlife and wildlife management in Tanzania. Conservation Biology. http://dx.doi.org/10.1111/cobi.12658
Carpenter, S. R., E. G. Booth, S. Gillon, C. J. Kucharik, S. Loheide, A. S. Mase, M. Motew, J. Qiu, A. R. Rissman, J. Seifert, E. Soylu, M. Turner, and C. B. Wardropper. 2015. Plausible futures of a social-ecological system: Yahara watershed, Wisconsin, USA. Ecology and Society 20(2):10. http://dx.doi.org/10.5751/es-07433-200210
Castella, J.-C., J. Bourgoin, G. Lestrelin, and B. Bouahom. 2014. A model of the science-practice-policy interface in participatory land-use planning: lessons from Laos. Landscape Ecology in Practice 29(6):1095-1107. http://dx.doi.org/10.1007/s10980-014-0043-x
Chhatre, A., S. Lakhanpal, A. M. Larson, F. Nelson, H. Ojha, and J. Rao. 2012. Social safeguards and co-benefits in REDD+: a review of the adjacent possible. Current Opinion in Environmental Sustainability 4(6):654-660. http://dx.doi.org/10.1016/j.cosust.2012.08.006
Danielsen, F., P. M. Jensen, N. D. Burgess, I. Coronado, S. Holt, M. K. Poulsen, R. M. Rueda, T. Skielboe, M. Enghoff, L. H. Hemmingsen, M. Sørensen, and K. Pirhofer-Walzl. 2014. Testing focus groups as a tool for connecting indigenous and local knowledge on abundance of natural resources with science-based land management systems. Conservation Letters 7(4):380-389. http://dx.doi.org/10.1111/conl.12100
Ellis, E. C. 2015. Ecology in an anthropogenic Biosphere. Ecological Monographs 85:287-331. http://dx.doi.org/10.1890/14-2274.1
Enfors, E. I., L. J. Gordon, G. D. Peterson, and D. Bossio. 2008. Making investments in dryland development work: participatory scenario planning in the Makanya Catchment, Tanzania. Ecology and Society 13(2):42. [online] URL: http://www.ecologyandsociety.org/vol13/iss2/art42/
Environmental Systems Research Institute (ESRI). 2014. ArcGIS Desktop: Release 10.2. ESRI, Redlands, California, USA.
Fisher, B., R. K. Turner, N. D. Burgess, R. D. Swetnam, J. Green, R. E. Green, G. Kajembe, K. Kulindwa, S. L. Lewis, R. Marchant, A. R. Marshall, S. Madoffe, P. K. T. Munishi, S. Morse-Jones, S. Mwakalila, J. Paavola, R. Naidoo, T. Ricketts, M. Rouget, S. Willcock, S. White, and A. Balmford. 2011. Measuring, modeling and mapping ecosystem services in the Eastern Arc Mountains of Tanzania. Progress in Physical Geography 35(5):595-611. http://dx.doi.org/10.1177/0309133311422968
Fraser, E. D. G., A. J. Dougill, W. E. Mabee, M. Reed, and P. McAlpine. 2006. Bottom up and top down: analysis of participatory processes for sustainability indicator identification as a pathway to community empowerment and sustainable environmental management. Journal of Environmental Management 78(2):114-127. http://dx.doi.org/10.1016/j.jenvman.2005.04.009
Freeman, R. E. 1984. Strategic management: a stakeholder approach. Pitman, Boston, Massachusetts, USA.
Green, J. M. H., C. Larrosa, N. D. Burgess, A. Balmford, A. Johnston, B. P. Mbilinyi, P. J. Platts, and L. Coad. 2013. Deforestation in an African biodiversity hotspot: extent, variation and the effectiveness of protected areas. Biological Conservation 164:62-72. http://dx.doi.org/10.1016/j.biocon.2013.04.016
Grêt-Regamey, A., S. H. Brunner, J. Altwegg, M. Christen, and P. Bebi. 2013. Integrating expert knowledge into mapping ecosystem services trade- offs for sustainable forest management. Ecology and Society 18(3):34. http://dx.doi.org/10.5751/ES-05800-180334
Griggs, D., M. Stafford Smith, J. Rockström, M. C. Öhman, O. Gaffney, G. Glaser, N. Kanie, I. Noble, W. Steffen, and P. Shyamsundar. 2014. An integrated framework for sustainable development goals. Ecology and Society 19(4):49. http://dx.doi.org/10.5751/ES-07082-190449
Hanspach, J., T. Hartel, A. I. Milcu, F. Mikulcak, I. Dorresteijn, J. Loos, H. von Wehrden, T. Kuemmerle, D. Abson, A. Kovács-Hostyánszki, A. Báldi, and J. Fischer. 2014. A holistic approach to studying social-ecological systems and its application to Southern Transylvania. Ecology and Society 19(4):32. http://dx.doi.org/10.5751/ES-06915-190432
Herrmann, S. M., I. Sall, and O. Sy. 2014. People and pixels in the Sahel: a study linking coarse-resolution remote sensing observations to land users’ perceptions of their changing environment in Senegal. Ecology and Society 19(3):29. http://dx.doi.org/10.5751/ES-06710-190329
Hesse, C., and J. MacGregor 2006. Pastoralism: drylands’ invisible asset? Developing a framework for assessing the value of pastoralism in East Africa. Issue Paper No 42. International Institute for Environment and Development, London, UK.
Intergovernmental Panel on Climate Change (IPCC). 2000. Special report on emissions scenarios. N. Nakicenovic and R. Swart, editors. Cambridge University Press, Cambridge, UK. [online] URL: http://www.grida.no/publications/other/ipcc_sr/?src=/climate/ipcc/emission/
Intergovernmental Panel on Climate Change (IPCC). 2007. Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, and H. L. Miller, editors. Cambridge University Press, Cambridge, UK.
International Union for Conservation of Nature and United Nations Environment Programme World Conservation Monitoring Centre (IUCN and UNEP-WCMC). 2015. The world database on protected areas (WDPA). Cambridge, UK. [online] URL: http://www.protectedplanet.net
Jew, E. K. K, A. J. Dougill, S. M. Sallu, J. O’Connell, T. G. Benton. 2016. Miombo woodland under threat: consequences for tree diversity and carbon storage. Forest Ecology and Management 361:144-153. http://dx.doi.org/10.1016/j.foreco.2015.11.011
Johnson, K. A., G. Dana, N. R. Jordan, K. J. Draeger, A. Kapuscinski, L. K. Schmitt Olabisi, and P. B. Reich. 2012. Using participatory scenarios to stimulate social learning for collaborative sustainable development. Ecology and Society 17(2):9. http://dx.doi.org/10.5751/ES-04780-170209
Kideghesho, J. R., A. A. Rija, K. A. Mwamende, and I. S. Selemani. 2013. Emerging issues and challenges in conservation of biodiversity in the rangelands of Tanzania. Nature Conservation 6:1-29. http://dx.doi.org/10.3897/natureconservation.6.5407
Lamarque, P., A. Artaux, C. Barnaud, L. Dobremez, B. Nettier, and S. Lavorel. 2013. Taking into account farmers’ decision making to map fine-scale land management adaptation to climate and socio-economic scenarios. Landscape and Urban Planning 119:147-157. http://dx.doi.org/10.1016/j.landurbplan.2013.07.012
Lange, S. 2008. Land tenure and mining in Tanzania. CMI Reports. Chr. Michelsen Institute, Bergen, Norway. [online] URL: http://www.cmi.no/publications/3008-land-tenure-and-mining-in-tanzania
Laurance, W. F., S. Sloan, L. Weng, J. A. Sayer. 2015. Estimating the environmental costs of Africa’s massive “development corridors.” Current Biology 25:3202–3208. http://dx.doi.org/10.1016/j.cub.2015.10.046
Leach, M., J. Rockström, P. Raskin, I. Scoones, A. C. Stirling, A. Smith, J. Thompson, E. Millstone, A. Ely, E. Arond, C. Folke, and P. Olsson. 2012. Transforming innovation for sustainability. Ecology and Society 17(2):11. http://dx.doi.org/10.5751/ES-04933-170211
Liu, J., H. Mooney, V. Hull, S. J. Davis, J. Gaskell, T. Hertel, J. Lubchenco, K. C. Seto, P. Gleick, C. Kremen, and S. Li. 2015. Systems integration for global sustainability. Science 347(6225). http://dx.doi.org/10.1126/science.1258832
Luyet, V., R. Schlaepfer, M. B. Parlange, and A. Buttler. 2012. A framework to implement stakeholder participation in environmental projects. Journal of Environmental Management 111:213-219. http://dx.doi.org/10.1016/j.jenvman.2012.06.026
Mahmoud, M., Y. Liu, H. Hartmann, S. Stewart, T. Wagener, D. Semmens, R. Stewart, H. Gupta, D. Dominguez, F. Dominguez, D. Hulse, R. Letcher, B. Rashleigh, C. Smith, R. Street, J. Ticehurst, M. Twery, H. van Delden, R. Waldick, D. White, and L. Winter. 2009. A formal framework for scenario development in support of environmental decision-making. Environmental Modelling & Software 24(7):798-808. http://dx.doi.org/10.1016/j.envsoft.2008.11.010
Malinga, R., L. J. Gordon, R. Lindborg, and G. Jewitt. 2013. Using participatory scenario planning to identify ecosystem services in changing landscapes. Ecology and Society 18(4):10. http://dx.doi.org/10.5751/ES-05494-180410
Meyfroidt, P., K. M. Carlson, M. E. Fagan, V. H. Gutiérrez-Vélez, M. N. Macedo, L. M. Curran, R. S. DeFries, G. A. Dyer, H. K. Gibbs, E. F. Lambin, D. C. Morton, and V. Robiglio. 2014. Multiple pathways of commodity crop expansion in tropical forest landscapes. Environmental Research Letters 9(7):074012. http://dx.doi.org/10.1088/1748-9326/9/7/074012
Milder, J. C., L. E. Buck, A. K. Hart, S. A. Shames, S. J. Scherr, and R. Kozar 2013. Six opportunities to green agricultural production in the southern agricultural growth corridor of Tanzania (SAGCOT). SAGCOT Centre, Dar es Salaam, Tanzania.
Ministry of Natural Resources and Tourism (MNRT). 2013. Tanzania mainland land use - land cover. Tanzania Forest Services Agency, Dar es Salaam, Tanzania.
Ministry of Natural Resources and Tourism (MNRT). 2015. National forest resource monitoring and assessment of Tanzania (NAFORMA). Main Results. Tanzania Forest Services Agency, Dar es Salaam, Tanzania.
Monitoring African Food and Agricultural Policy (MAFAP). 2013. Review of food and agricultural policies in the United Republic of Tanzania. Country Report Series. FAO, Rome, Italy.
Moyo, M., R. Simson, A. Jacob, and F. De Mevius. 2012. Attaining middle income status - Tanzania: growth and structural transformation required to reach middle income status by 2025. Working paper 11/1019, International Growth Centre, Oxford, UK.
Mustalahti, I., A. Bolin, E. Boyd, and J. Paavola. 2012. Can REDD+ reconcile local priorities and needs with global mitigation benefits? Lessons from Angai Forest, Tanzania. Ecology and Society 17(1):16. http://dx.doi.org/10.5751/ES-04498-170116
Myers, N., R. A. Mittermeier, C. G. Mittermeier, G. A. B. Fonseca, and J. Kent. 2000. Biodiversity hotspots for conservation priorities. Nature 403:853-858. http://dx.doi.org/10.1038/35002501
National Bureau of Statistics (NBS), and Office of Chief Government Statistician (OCGS), Zanzibar. 2013. The 2012 population and housing census: population distribution by administrative area. NBS-OCGS, Dar es Salaam, Tanzania.
National Bureau of Statistics (NBS), and Office of Chief Government Statistician (OCGS), Zanzibar. 2014. The 2012 population and housing census: basic demographic and socio- economic profile: key findings. NBS-OCGS, Dar es Salaam, Tanzania.
Organisation for Economic Co-operation and Development (OECD). 2013. OECD investment policy reviews: Tanzania 2013. OECD, Paris, France.
Ostberg, S., S. Schaphoff, W. Lucht, and D. Gerten. 2015. Three centuries of dual pressure from land use and climate change on the biosphere. Environmental Research Letters 10(4):44011. http://dx.doi.org/10.1088/1748-9326/10/4/044011
Oteros-Rozas, E., B. Martín-López, T. Daw, E. L. Bohensky, J. Butler, R. Hill, J. Martin-Ortega, A. Quinlan, F. Ravera, I. Ruiz-Mallén, M. Thyresson, J. Mistry, I. Palomo, G. D. Peterson, T. Plieninger, K. A. Waylen, D. Beach, I. C. Bohnet, M. Hamann, J. Hanspach, K. Hubacek, S. Lavorel, and S. Vilardy. 2015. Participatory scenario planning in place-based social-ecological research: insights and experiences from 23 case studies. Ecology and Society 20(4):32. http://dx.doi.org/10.5751/ES-07985-200432
Parker, D. C., A. Hessl, and S. C. Davis. 2008. Complexity, land-use modeling, and the human dimension: fundamental challenges for mapping unknown outcome spaces. Geoforum 39(2):789-804. http://dx.doi.org/10.1016/j.geoforum.2007.05.005
Peterson, G. D., G. S. Cumming, and S. R. Carpenter. 2003. Scenario planning: a tool for conservation in an uncertain world. Conservation Biology 17(2):358-366. http://dx.doi.org/10.1046/j.1523-1739.2003.01491.x
Pfeifer, M., P. J. Platts, N. D. Burgess, R. D. Swetnam, S. Willcock, S. L. Lewis, and R. Marchant. 2013. Land use change and carbon fluxes in East Africa quantified using earth observation data and field measurements. Environmental Conservation 40(3):241-252. http://dx.doi.org/10.1017/S0376892912000379
Priess, J. A., and J. Hauck. 2014. Integrative scenario development. Ecology and Society 19(1):12. http://dx.doi.org/10.5751/ES-06168-190112
R Core Team. 2014. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [online] URL: http://www.R-project.org/
Reed, M. S., J. Kenter, A. Bonn, K. Broad, T. P. Burt, I. R. Fazey, E. D. G. Fraser, K. Hubacek, D. Nainggolan, C. H. Quinn, L. C. Stringer, and F. Ravera. 2013. Participatory scenario development for environmental management: a methodological framework illustrated with experience from the UK uplands. Journal of Environmental Management 128:345-362. http://dx.doi.org/10.1016/j.jenvman.2013.05.016
Reidsma, P., H. König, S. Feng, I. Bezlepkina, I. Nesheim, M. Bonin, M. Sghaier, S. Purushothaman, S. Sieber, M. K. van Ittersum, and F. Brouwer. 2011. Methods and tools for integrated assessment of land use policies on sustainable development in developing countries. Land Use Policy 28(3):604-617. http://dx.doi.org/10.1016/j.landusepol.2010.11.009
Riedler, B., L. Pernkopf, T. Strasser, S. Lang, and G. Smith. 2015. A composite indicator for assessing habitat quality of riparian forests derived from Earth observation data. International Journal of Applied Earth Observation and Geoinformation 37:114-123. http://dx.doi.org/10.1016/j.jag.2014.09.006
Rosenberg, M., R.-U. Syrbe, J. Vowinckel, and U. Walz. 2014. Scenario methodology for modelling of future landscape developments as basis for assessing ecosystem services. Landscape Online 33(1):1-20. http://dx.doi.org/10.3097/LO.201433
Rounsevell, M. D. A., B. Pedroli, K.-H. Erb, M. Gramberger, A. G. Busck, H. Haberl, S. Kristensen, T. Kuemmerle, S. Lavorel, M. Lindner, H. Lotze-Campen, M. J. Metzger, D. Murray-Rust, A. Popp, M. Pérez-Soba, A. Reenberg, A. Vadineanu, P. H. Verburg, and B. Wolfslehner. 2012. Challenges for land system science. Land Use Policy 29(4):899-910. http://dx.doi.org/10.1016/j.landusepol.2012.01.007
Rulli, M. C., A. Saviori, and P. D’Odorico. 2103. Global land and water grabbing. Proceedings of the National Academy of Sciences 110(3):892-897. http://dx.doi.org/10.1073/pnas.1213163110
Saisana, M., and S. Tarantola. 2002. State-of-the-art report on current methodologies and practices for composite indicator development. EUR 20408 EN. European Commission, Joint Research Centre, Ispra, Italy.
Sander, K., C. Gros, and C. Peter. 2013. Enabling reforms: analyzing the political economy of the charcoal sector in Tanzania. Energy for Sustainable Development 17(2):116-126. http://dx.doi.org/10.1016/j.esd.2012.11.005
Sandker, M., B. M. Campbell, M. Ruiz-Pérez, J. A. Sayer, R. Cowling, H. Kassa, and A. T. Knight. 2010. The role of participatory modeling in landscape approaches to reconcile conservation and development. Ecology and Society 15(2):13. [online] URL: http://www.ecologyandsociety.org/vol15/iss2/art13/
Sohl, T. L., and P. R. Claggett. 2013. Clarity versus complexity: land-use modeling as a practical tool for decision-makers. Journal of Environmental Management 129:235-243. http://dx.doi.org/10.1016/j.jenvman.2013.07.027
Strachan, A. L. 2015. Women in politics and the public sector in Tanzania. GSDRC Helpdesk Research Report 1286. GSDRC, University of Birmingham, Birmingham, UK. [online] URL: http://www.gsdrc.org/publications/women-in-politics-and-the-public-sector-in-tanzania/
Stringer, L. C., A. J. Dougill, E. Fraser, K. Hubacek, C. Prell, and M. S. Reed. 2006. Unpacking “participation” in the adaptive management of social-ecological systems: a critical review. Ecology and Society 11(2):39. [online] URL: http://www.ecologyandsociety.org/vol11/iss2/art39/
Swetnam, R. D., B. Fisher, B. P. Mbilinyi, P. K. T. Munishi, S. Willcock, T. Ricketts, S. Mwakalila, A. Balmford, N. D. Burgess, A. R. Marshall, and S. L. Lewis. 2011. Mapping socio-economic scenarios of land cover change: a GIS method to enable ecosystem service modelling. Journal of Environmental Management 92(3):563-574. http://dx.doi.org/10.1016/j.jenvman.2010.09.007
Tschakert, P., and K. A. Dietrich. 2010. Anticipatory learning for climate change adaptation and resilience. Ecology and Society 15(2):11. [online] URL: http://www.ecologyandsociety.org/vol15/iss2/art11/
Turner, B. L., E. F. Lambin, and A. Reenberg. 2007. The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences 104:20666-20671. http://dx.doi.org/10.1073/pnas.0704119104
United Nations (UN). 2012. The future we want: outcome document of the United Nations conference on sustainable development. United Nations, New York, New York, USA. [online] URL: https://sustainabledevelopment.un.org/content/documents/733FutureWeWant.pdf
United Nations Conference on Environment and Development (UNCED). 1992. Earth Summit Agenda 21. The United Nations Programme of Action from Rio. UN, New York, New York, USA. [online] URL: https://sustainabledevelopment.un.org/content/documents/Agenda21.pdf
United Nations Department of Economics and Social Affairs (UNDESA). 2015. A/RES/70/1 - Transforming our world: the 2030 agenda for sustainable development. UN, New York, New York, USA. [online] URL: http://www.un.org/ga/search/view_doc.asp?symbol=A/RES/70/1&Lang=E
United Nations Environmental Programme (UNEP). 2010. Strategic plan for biodiversity 2011-2020 and the Aichi biodiversity targets. UNEP/CBD/COP/DEC/X/2. Conference of the parties to the convention on biological diversity, 18-29 October 2010, Nagoya, Japan. [online] URL: https://www.cbd.int/doc/decisions/cop-10/cop-10-dec-02-en.pdf
United Nations Environmental Programme (UNEP). 2012. Global Environment Outlook 5. UNEP, Nairobi, Kenya. [online] URL: http://www.unep.org/geo/geo5.asp
United Nations Environmental Programme (UNEP). 2015. Forest ecosystems in the transition to a green economy and the role of REDD+ in the United Republic of Tanzania. UNEP, Nairobi, Kenya. [online] URL: http://www.unredd.net/index.php?view=document&alias=14392-forest-ecosystemin-the-transition-to-a-green-economy-and-the-role-of-redd-in-the-united-republic-of-tanzania&category_slug=forest-ecosystem-valuation-and-economics&layout=default&option=com_docman&Itemid=134
United Republic of Tanzania (URT). 2005. Tanzania development vision 2025. Planning Commission, Dar es Salaam, Tanzania. [online] URL: http://www.tzonline.org/pdf/theTanzaniadevelopmentvision.pdf
United Republic of Tanzania (URT). 2011. The economic survey 2011. Planning Commission, Dar es Salaam, Tanzania.
United Republic of Tanzania (URT). 2013b. National environmental action plan (NEAP): 2013-2018. Vice President’s Office, Dar es Salaam, Tanzania. [online] URL: http://www.vpo.go.tz/userfiles/NEAP%20B5.pdf
United Republic of Tanzania (URT). 2013a. National strategy for reduced emissions from deforestation and forest degradation (REDD+). Vice President’s Office, Dar es Salaam, Tanzania.
United Republic of Tanzania (URT). 2014. Status of land degradation in Tanzania. Vice President’s Office, Dar es Salaam, Tanzania.
United Republic of Tanzania (URT), Ministry of Agriculture, Food security and Cooperation (MAFSC). 2013. Agstats for food security. Preliminary food crop production forecast for 2013 / 14. Food Security Crop Monitoring and Early Warning, Dar es Salaam, Tanzania.
United States Agency for International Development (USAID). 2011. USAID country profile. Property rights and resource governance profile: Tanzania. USAID, Wasington, D.C., USA. [online] URL: http://www.usaidlandtenure.net/sites/default/files/country-profiles/full-reports/USAID_Land_Tenure_Tanzania_Profile.pdf
van Vuuren, D. P., M. Kok, P. L. Lucas, A. G. Prins, R. Alkemade, M. van den Berg, L. Bouwman, S. van der Esch, M. Jeuken, T. Kram, and E. Stehfest. 2015. Pathways to achieve a set of ambitious global sustainability objectives by 2050: explorations using the IMAGE integrated assessment model. Technological Forecasting and Social Change 98:303-323. http://dx.doi.org/10.1016/j.techfore.2015.03.005
Verburg, P. H., K.-H. Erb, O. Mertz, and G. Espindola. 2013. Land system science: between global challenges and local realities. Current Opinion in Environmental Sustainability 5(5):433-437. http://dx.doi.org/10.1016/j.cosust.2013.08.001
Walz, A., C. Lardelli, H. Behrendt, A. Grêt-Regamey, C. Lundström, S. Kytzia, and P. Bebi. 2007. Participatory scenario analysis for integrated regional modelling. Landscape and Urban Planning 81(1-2):114-131. http://dx.doi.org/10.1016/j.landurbplan.2006.11.001
Weng, L., A. K. Boedhihartono, P. H. G. M. Dirks, J. Dixon, M. I. Lubis, and J. A. Sayer. 2013. Mineral industries, growth corridors and agricultural development in Africa. Global Food Security 2(3):195-202. http://dx.doi.org/10.1016/j.gfs.2013.07.003
Willcock, S., O. L. Phillips, P. J. Platts, R. D. Swetnam, A. Balmford, N. D. Burgess, A. Ahrends, J. Bayliss, N. Doggart, K. Doody, E. Fanning, J. M. H. Green, J. Hall, K. L. Howell, J. C. Lovett, R. Marchant, A. R. Marshall, B. Mbilinyi, P. K. T. Munishi, N. Owen, E. J. Topp-Jorgensen, and S. L. Lewis. 2016. Land cover change and carbon emissions over 100 years in an African biodiversity hotspot. Global Change Biology 22(8):2787-2800. http://dx.doi.org/10.1111/gcb.13218
World Bank. 2008. Gender in agriculture sourcebook. Agriculture and Rural Development. World Bank. Washington, D.C., USA. [online] URL: http://documents.worldbank.org/curated/en/2008/10/9953789/gender-agriculture-sourcebook http://dx.doi.org/10.1596/978-0-8213-7587-7
World Bank. 2014. World data bank: Tanzania. World Bank, Washington, D.C., USA. [online] URL: http://data.worldbank.org/country/tanzania
World Wildlife Fund Tanzania Country Office (WWF-TCO). 2015. WWF REDD+ pilot project. Final project report. WWF, Dar es Salaam, Tanzania. [online] URL: http://d2ouvy59p0dg6k.cloudfront.net/downloads/wwf___redd__final_project_report___10th_april_2015_1.pdf
Zisenis, M. 2014. The International Platform on Biodiversity and Ecosystem Services gets profile. Biodiversity and Conservation 24(1):199-203. http://dx.doi.org/10.1007/s10531-014-0797-0