Nexus thinking is a call to overcome tunnel vision. It asks us to critically analyze water, energy, and food resource interconnections and anticipate how changing water-energy-food interactions may instigate, accelerate, or intensify complex system transitions (e.g., Scheffer et al. 2012) and other risks (Hoff 2011). Yet, while considered promising, nexus thinking is currently criticized as not “fit for purpose” when it comes to real situations of deeply entangled and contested governance agendas (Al-Saidi and Elagib 2017).
“Risk” is the probability and severity of consequences from changing framework conditions, for example, in system regimes that affect hazard likelihood, exposure, and vulnerability (Haimes 2009). In the nexus, such consequences manifest differently across actors, scales, and time frames and depend on factors like severity of risk events, who is affected, and their risk tolerances (Gallagher et al. 2016, Grafton et al. 2016). How is risk assessed and allocated fairly where there is low consensus on priorities, problems, and varying vulnerabilities (Weitz et al. 2017)? Recent water governance research (Bouckaert et al. 2018, Pahl-Wostl 2019) underscores the importance of engaging with such uncertainties (Guston 2014) but nexus research has a less developed theoretical focus on adaptive governance. Computational modeling dominates this field (Albrecht et al. 2018, Shannak et al. 2018). Such methods help anticipate some consequences of water-energy-food interconnections but do not consider stakeholder perspectives deeply, if at all (Al-Saidi and Elagib 2017, Hagemann and Kirschke 2017, Larcom and van Gevelt 2017).
We consider that risk assessments in nexus research and policy need to grapple with uncertain and unknown stakeholder values and capacities (Yung et al. 2019) and varying risk perceptions (Howarth and Monasterolo 2017, Weitz et al. 2017), as well as changing states of the system being studied (Scheffer et al. 2012). In this paper we share our experience innovating on one method with great potential in this regard at a sub-basin scale in the Mekong river basin in Southeast Asia.
The LIVES project (http://livesproject21.org/) set out to conduct fundamental research on mixed methods approaches for identifying indicators that reflect interdependencies between food, energy, and water and develop understanding of social-ecological system inflection points. Our goal was to innovate a knowledge coproduction method that enables diverse stakeholders to be actively involved in identifying these indicators and inflection points while creating new understanding of trade-offs from multiple actors’ perspectives and momentum for seeking solutions. Our departure point was to devise a participatory model-based scenario planning approach based on both futures and resilience thinking (Walker et al. 2004, Foran et al. 2013, Gerritsen et al. 2013, Guston 2014, Boyd et al. 2015, van der Voorn et al. 2017) with scope to include multiple stakeholders (Weber 1997, Klinke and Renn 2012) in a flexible yet robust research process (Rijke et al. 2012, Pahl-Wostl 2019). Our research takes up the thread of Foran et al. (2013), Foran (2015), and Smajgl et al. (2016) with model-based scenario planning assessment of hydropower development in one landscape in Cambodia.
It has been argued that participatory system dynamics modeling has potential to create new knowledge about risks that is accepted by stakeholders as their knowledge (e.g., Basco-Carrera et al. 2017). Some sustainability science research supports this supposition (Innes and Booher 2010, Clark et al. 2016, Rouwette 2016). Well implemented, participatory research can certainly contribute to flexible strategies that consider long-term goals and consequences under several possible futures (Gerritsen et al. 2013, Guston 2014, Boyd et al. 2015, van der Voorn et al. 2017). Yet, we still struggle with including stakeholders from outside the technocratic policy world effectively and fairly (Voinov et al. 2016, Jordan et al. 2018). With these challenges firmly in mind, the specific objectives for our three-year process were the following:
We report a reflexive analysis of results and feedback from our partners and participants as a contribution to continuing innovation in nexus research. Rather than applied research, we consider this work a contribution to fundamental research on nexus methods because of its novel characteristics. Where other Mekong nexus studies advance horizontal policy and actor network integration at national and basin-scales (Foran et al. 2013, Smajgl and Ward 2013, Smajgl et al. 2015, 2016, Pittock et al. 2016), we explored both vertical and horizontal integration between national and provincial-levels in participatory risk assessments. The explicit risk lens in our study is rare in empirical nexus research (Grafton et al. 2016), and our chosen computational modeling method, system dynamics modeling, has not yet been applied in the Mekong region in participatory form (Bassi et al. 2016, Chapman and Darby 2016, Pittock et al. 2016) to the best of our knowledge and has some interesting complementarities to participatory agent-based modeling approaches previously tested in the region (e.g., Smajgl et al. 2015).
The Mekong is a busy testing ground for conceptual and analytical frameworks in nexus research (Foran et al. 2013, Smajgl and Ward 2013, Foran 2015, Middleton et al. 2015, Smajgl et al. 2015, Pittock et al. 2016, Lebel and Lebel 2018) because it is a region where large-scale, uncoordinated hydropower development, climate, and socioeconomic change converge in a biodiverse social-ecological system to impact on livelihoods, water, and food security (Molle et al. 2012, Middleton et al. 2015, MRC 2017, Fox and Sneddon 2019).
Two provinces in northeastern Cambodia, Kratie and Stung Treng provinces, have been experiencing rapid change through forest clearance for rubber plantation, river bed sediment mining, road network infrastructure, and climate change impacts (RGC 2011a, b). The provincial administrations comanage parts of the Mekong Flooded Forest Landscape, a transboundary biodiversity conservation landscape hereafter referred to as the MFF Landscape (Champasak province in neighboring Lao PDR is also part of the landscape but is excluded to focus on Cambodian jurisdiction in this research). At the time of this research, two major Cambodian hydropower projects, Stung Treng dam (Stung Treng province) and Sambor dam (Kratie province), were at proposal stage with physical construction imminent in the landscape though with little information being shared publicly with local communities. Both projects are currently on hold under the new moratorium on hydropower development in the central Mekong channel in Cambodia (Ratcliffe 2020).
Increased energy supply is a priority under the Royal Government of Cambodia’s development plans because of high domestic energy costs and low rates of energy access (RGC 2016a, b). Hydropower is considered to be the main domestic renewable energy option available to improve energy security (RCG 2016a, b, c). From the provincial administration, commune administration, and community perspectives, the change in the Mekong River’s flow means unpredictable change to the flood regime, fish migration patterns, and biodiversity given observed climate change effects (RGC 2016d, MRC 2017).
Risk-based management is limited in Cambodia with low availability and sharing of local risk information (Mochizuki et al. 2015). This fact, along with differences in local and national priorities, power differentials, and other complex cultural, political, and historical factors domestically (Milne and Mahanty 2015) and regionally (Molle et al. 2012, Urban et al. 2015, Villamayor-Tomas et al. 2016) means risks and opportunities are assessed most consistently by powerful national line ministries in relation to regional energy market dynamics, energy security, and industrial development. The result is a poor consideration of how dams could contribute to local, national, and regional food, livelihoods, and other insecurities (Sithirith 2016).
Our transdisciplinary research design (Lang et al. 2012) applied participatory system dynamics modeling (Videira et al. 2010) and a new resilience analysis method (Herrera 2017) to analyze anticipated water-energy-food risks in Kratie and Stung Treng provinces. We identified major elements in the local water-energy-food nexus structure with stakeholders—the variables and interconnections that are relevant to understand water, energy and food production and stakeholder priorities and perceived risks—and then analyzed the development and resilience of these under various scenarios.
The General Secretariat to the National Council for Sustainable Development (NSCD) was our national government partner. NSCD is a key stakeholder because of their position as a cross-ministerial body with the mandate to prepare, coordinate, and monitor implementation of policies, strategies, legal instruments, plans, and programs related to sustainable development in Cambodia. Other key partners included WWF Cambodia, a civil society organization operating in the MFF Landscape, the Royal University of Phnom Penh (RUPP), and the Royal University of Agriculture (RUA)—civil society actors active on the water-energy-food and biodiversity trade-offs both nationally and in the case provinces, with networks and legitimacy to convene government actors, local communities, and local civil society organizations in Kratie and Stung Treng provinces. WWF, with their long-standing engagement structures and relationships with the NCSD, the Ministry of Environment, and both provincial administrations, issued the formal project workshop invitations and managed project stakeholder networks.
Our stakeholder identification procedures were implemented iteratively throughout our research process. The partners agreed that an essential starting point was to begin with actors with a stake in development planning processes at commune level in the MFF Landscape.
National development and commune investment planning processes are the formal governance mechanisms both anticipating and driving changes in economic, social, and environmental conditions in the provinces. However, the planning process is fragmented across several national line ministries. In theory, local-level priorities are identified in the long-standing Commune Investment Planning (CIP) processes, guided by Ministry of Interior rules on procedures, and rolled up through district and provincial administration departments to their national line ministry and integrated in National Strategic Development Plan (NSDP) every five years. In practice, little horizontal or vertical integration takes place in planning (Vuković and Babović 2018) and there are concerns about how the process works in practice (World Bank and The Asia Foundation 2013, Siciliano et al. 2015). A process of decentralization and deconcentration of government functions (hereafter: D&D reforms) is devolving some national line ministry functions from the national Ministry of Environment to the Provincial Departments of Environment, though public finance is still centralized at national level (Vuković and Babović 2018). Between the CIP and its development outcomes (World Bank and The Asia Foundation 2013) and the emergence of the provincial level as a new significant jurisdictional scale, the research team identified the provincial government administrations as a critical group of stakeholders to work with on the MFF nexus assessment.
Provincial administrations are actively requesting support to develop new capacities to undertake new mandates being received under D&D reforms. This perhaps explains the consistent attendance of government officials from 10 departments and executive-level offices in provincial administrations, including the Deputy Governor offices in our research process. These actors also represent business and broader community interests to some extent, given that low government salaries means essentially all government employees can be assumed to have some other economic interests ongoing: farming, commerce, property investment. D&D provincial program representatives from the national Ministry of Interior participated in every workshop. We invited local civil society groups to participate alongside local government participants in representing these community concerns. At later stages in the research process we invited farming and fishing community representatives to separate workshops (reported in Kimmich et al. 2019). We did this in awareness of power dynamics arising from visible and invisible social, political, and cultural structures (e.g., Bréthaut et al. 2019) and power distribution in research processes (e.g., Pohl et al. 2010), which can influence what information is shared and how it is interpreted in wicked problem contexts (Parkhurst 2016) where data poverty is a concern (Johnston et al. 2013). We ran all workshops in Khmer, with a mix of facilitators from the government, civil society, and academic partners. We held separate events for different stakeholder groups where we thought hierarchy would influence contributions. We requested anonymous feedback surveys at the end of each workshop. All research team partners participated as knowledge contributors when not fulfilling the roles of trainers or facilitators.
System dynamics modeling creates explanatory models of system structures and simulates dynamic interplay between key variables to explore system behavior over time (Forrester 1961, Sterman 2000). The method helps system conceptualization and problem identification in social-ecological systems where simulation, not optimization, is most useful for decision making (e.g., Videira et al. 2010, Kopainsky et al. 2017). Such models facilitate knowledge integration across many domains (Harwood 2018), shedding light on interactions between social and natural systems and how these might be influenced by public policy (Ghaffarzadegan et al. 2010).
The system dynamics model was developed using a participatory modeling procedure (Fig. 1). We identified and quantified the mechanisms underlying trade-offs between national level energy security and economic growth and local level food security, the priority risks (scenarios), and potential actions (interventions) in an iterative process between stakeholder engagement and desk research. Stakeholder engagement involved five participatory group model building workshops held between Phnom Penh, Kratie, and Stung Treng between January 2015 and July 2016, bilateral meetings, and additional expert interviews to close knowledge and data gaps. Other follow-up workshops with local farming and fishing communities included a small number of provincial officials during 2017. A final workshop was held in Phnom Penh in December 2017 where preliminary analysis results were presented to national government representatives from Ministries of the Interior, Environment, among others (see Appendix 1 for a detailed overview of meetings).
Stakeholders coproduced multiple causal loop diagrams (CLDs; Hovmand et al. 2012, Voinov et al. 2016), identifying major variables and interconnections that characterize the dynamic complexity of the MFF landscape, including the major nexus interrelationships between water, energy, food resources, and links with ecosystems.
The nexus concept was new for all our stakeholder groups, as was the CLD procedure. A team of national researchers, national government staff, and local WWF staff were trained in facilitating preliminary values and threats analysis and CLD scripts, working alongside international researchers to facilitate discussions in Khmer. Following Luna-Reyes et al. (2006), stakeholders’ CLDs were analyzed to elicit (i) stakeholder assumptions, perceptions about critical variables for the MFF nexus, and their interactions; (ii) priority concerns held by these stakeholders; and (iii) possible intervention points that were then aggregated and used to inform the computational model-building.
Figure 2 illustrates the aggregated output of these procedures across all stakeholder workshops. It illustrates the interconnections between water, energy, and food security, as well as economic growth, and a series of critical feedback loops. A first feedback loop connects an increase in crop production with an increase in gross domestic product (GDP). This, in turn, stimulates food demand. Parts of increased food demand are covered by increasing deforestation, which leads to an increase in agricultural land and thus to crop production. Ceteris paribus, this feedback loop reinforces food production and economic growth. A second example of a feedback loop described in Figure 2 is the balancing mechanism introduced by an increase in food demand, which leads to an increase in fish catch and a corresponding decrease in fish stocks. Declining fish stocks erode the food base for dolphins that are an important attractor for tourism. With declining dolphin populations, tourist arrivals go down and therefore reduce GDP. This balancing feedback loop limits the growth of the reinforcing feedback loop between food production and GDP. Validation procedures in this process included prioritizing multiple mentions of similar themes and comparison with other data sources relevant to the Mekong Nexus (e.g., Pittock et al. 2016, as per recommendations of Voinov et al. 2016).
The full CLD developed with stakeholders includes a large number of feedback loops that all interact with each other. For scenario and policy analysis, it is thus important to quantify the relationships described in the CLDs and translate them into a running simulation model, hereafter referred to as the MFF mode.
The MFF model simulates water-energy-food interactions for Kratie and Stung Treng provinces from 2000 to 2040 with local central river channel hydropower dam construction as the primary trigger for risks. Model equations were sourced from existing models, peer-reviewed papers, and technical reports and iterative consultation with regional, national, and provincial stakeholders and other experts (see Appendix 2). Both system-wide and sectoral calibrations were performed. The model was validated (Barlas 1996) through formal structural and behavioral validation as well as stakeholder review of simulation results during 2016 and 2017. The model is appropriate for aggregated analysis of governance responses to be negotiated and agreed, e.g., improve crop yields, but is not yet suitable for policy design in its current form, e.g., deciding levels of investment in crop breeding. Our priority is to understand the main interactions between water, energy, food, and other important dynamics first to identify the main risks and main potential unanticipated consequences of interventions arising from system structure and behavior. The MFF model has two important limitations in this regard: (i) it is not spatially disaggregated, nor is it possible to simulate the behavior of individuals or households as with agent-based modeling; (ii) a poor representation of health domain participants in our process means health variables are weakly represented in the model.
Our analytical strategy uses three distinct procedures.
1. Qualitative data analysis. A substantial amount of qualitative data was amassed during the project and analyzed to support the choice of indicators to represent stakeholder priorities and identified risks of concern (hereafter: risk indicators), scenario formulation, resilience analysis design, and in the discussion of results. Interviews with provincial administration officials, were conducted in 2015 and again in 2017 (document code: CA). Workshop reports and stakeholder feedback sheets (document code: SF) were also produced. In addition, 15 Most Significant Change interviews (Dart and Davies 2003) conducted in November and December 2017 elicited project partners’ observations about overall changes catalyzed by the research (document code: MSC; see Appendix 1).
2. Scenario analysis. We analyzed outcomes for stakeholder priorities from changing framework conditions triggered by the Stung Treng hydropower development by comparing simulation runs using Vensim simulation software (note that the figures were produced using Stella software). In total, we created four scenarios for analysis. See Table 1 for a description and key assumptions for each.
We calibrated two baseline scenarios, one scenario without the dam and one for future development in the region with Stung Treng Dam. This assessment of baseline trends is important to assess the multidimensional impacts of hydropower dam construction. We focused primarily on Stung Treng dam because at the time of our workshops the construction time line of Sambor dam had not been confirmed. Sambor dam is treated as an “additional hydropower investment” in the resilience analysis. Provincial officials view infrastructure development as a critical enabler of new inward investment flows to the region (CA2015_1). Stung Treng dam is assumed to trigger more natural resources extraction and consumption, and new business activity, in our CLD groups, though stakeholder attitudes to dam development were mixed. There was some confusion about the planned timing and actual sites, and whether the dams will supply domestic or regional energy markets. Stung Treng dam, a proposed mainstem (central river channel) gravity dam, is primarily intended to produce energy for export to Thailand. Sambor dam is communicated as a far-distant development project rather than an impending reality by senior provincial government officials (MSC3). The project team agreed to work with what provincial administration officials have understood from national line ministries as assumptions in model building and explore them in the simulation results: (i) dam construction would employ local workers, (ii) some new power capacity created by the hydropower developments could be redirected to the local economy, and (iii) supporting roads will market access for local agricultural products.
Second, we assessed two scenarios for adaptation (an alternative cropping scheme) and a mitigation (environmental flow standards) options to mitigate and respond to risks being created in this local manifestation of the Mekong nexus. The mitigation and adaptation interventions were identified by two separate stakeholder groups. Crop and fish production interventions were identified by both provincial administration stakeholders and farming community representatives in mixed CLD sessions (SF5.1_2017.03.13.), with crop interventions targeting increased rice yields as is heavily promoted by national government policy (Ly et al. 2012, 2016, UN FAOSTAT 2018). Environmental flows technologies were proposed by WWF staff as one mitigation approach to explore, as per Poff and Matthews (2013).
3. Resilience analysis. Given uncertainties surfaced in stakeholder discussions and model calibration, we complemented the model-based scenario analysis with a resilience analysis.
Critical future system disturbances of concern to the national government, provincial administration, and civil society stakeholders were climate change and population dynamics. Civil society groups were also concerned about additional dam investments for Sambor dam being represented in the analysis. For the purpose of our analysis, these disturbances are defined (see Table 2) as (a) a change in rainfall variability (climate change); (b) change in the absolute population growth rate; and (c) additional investment levels beyond the Stung Treng dam, anticipating the potential Sambor dam development.
We analyzed the amount of disturbance (deviation from scenario assumptions) needed to change the MFF system from the starting simulated scenario state to another state using two specific resilience metrics (as per Herrera 2017):
We ran Monte Carlo analyses and varied rainfall minimums around average rainfall projections from climate models to change the uniform random distribution of rainfall variability, the magnitude of net migration rate and additional investment in hydropower to assess disturbance from new dams that may be built in the MFF after the Stung Treng dam. We then calculated the percentage deviation of our risk indicators from their reference value (the value produced by the baseline simulations) for each of 200 Monte Carlo runs. After ordering the simulation runs by size of disturbance, the maximum disturbance that the risk indicators could tolerate before (1) deviating significantly (at 5% confidence bound) from the baseline behavior (hardness) or (2) transforming so that the indicator behavior never returns to baseline behavior after the disturbance (elasticity) was manually identified. We performed this resilience analysis for several indicators that reflect the different priorities different stakeholder groups have for nexus development. We refrained from an overall resilience assessment because that would have implied assigning weights to each of these priorities.
Poverty was consistently identified as a major threat, and poverty reduction as the most important priority, across individual stakeholders and stakeholder groups in all CLD procedures. Generally speaking, agriculture and fisheries management are seen as important development pathways for the provinces (CA2015_6), though participants were highly pessimistic about the state of local fisheries and concerned about water availability for agricultural activities into the future. Table 3 displays priorities for Kratie and Stung Treng stakeholders and associated indicators within the MFF model selected by the research analysts for the scenario and resilience analyses.
Our simulation modeling focuses on how these indicators perform. Our analysis confirms some expected trade-offs between national-level energy security and economic growth and MFF food, livelihoods, and water security, while also revealing a new understanding of some drivers of these outcomes. We report the most surprising results and the nuances generated by the dynamic modeling in reporting the scenario and resilience modeling outputs.
Scenario results are shown in two different forms in Figure 3: graph (a) displays the time-dependent values of the absolute indicator values while graph (b) compares the scenarios—baseline with Stung Treng dam, adaptation (dam + higher rice yields), and mitigation (dam + E-flows)—to the behavior of our reference scenario, the “baseline without dam.”
Negative effects on crop self-sufficiency (Fig. 3a) are not as strong as might be expected given dam impacts on water and land availability. The main reason is an assumption made in the model that further agricultural land will be available for cultivation if crop production productivity reduces in the future because of reduced extent of seasonal inundation.
Reduced sediment flows are assumed to lower availability of organic sources of nutrients to agricultural activities. Because of lack of affordability, all nutrients required to maintain productivity cannot be provided by additional mineral fertilizer purchases and thus, more agriculture land will be sought. Forest land is the main land type converted, which would lead to a decline in a variety of ecosystem services that are hard to quantify.
In the alternative crops scenario, agricultural productivity increases substantially on existing agricultural land, which reduces the pressure to convert forest land. Introducing higher rice yields leads to some improvements but these fade over time (cf. convergence between the lines for “base with dam” and “dam + alternative crops” in Fig. 3.1b). Crop production is similar across these scenarios because forest land to agricultural cultivation in “base with dam” scenario produces similar production effects as higher rice yields being sought on existing agricultural land.
The introduction of high environmental flow requirements (e-flows) has the largest mitigating impact on crop self-sufficiency risks. The main driver behind these differences is the assumed nutrient availability for crop growth embodied in flow sediments. Higher e-flows also mitigate some of the negative impacts of the hydropower dam on fish self-sufficiency (Fig 3.2a). This mitigation scenario also results in higher energy self-sufficiency than observed in the “base without dam” scenario. Interestingly, this scenario improves per capita income compared to the other scenarios including the dam. This is due a combination of two processes: (1) an increase in crop production and (b) a more stable generation of hydropower with its subsequent beneficial impacts on economic activities at large. The planned hydropower expansion leads to improvement in local energy self-sufficiency only under an assumption that at least 15% of the new power capacity is allocated for local use (Fig. 3a and b).
A principal point is that model results indicate some trade-offs in crop self-sufficiency, fish self-sufficiency, and energy self-sufficiency in all scenarios, and not just those with dam development. Water consumption (relative to the year 2000) also increases in all scenarios, but somewhat less where agricultural production is restrained because land availability reduces after dam development. Per capita income increases in the short run for all scenarios with the construction of the Stung Treng dam because stakeholders assume local employment increases as the dam is constructed. The growth rate drops as soon as dam construction is completed however. Furthermore, after dam construction, the minor gains in additional income from an increase in economic productivity suggested by the model is somewhat dampened by a 15% reduction in the value of food production in the “base with dam” and “dam alternative crops” scenarios.
For climate change (Table 4), a low hardness value of 6.3 for crop self-sufficiency in the “base no dam” indicates that a reduction in the rainfall minimum by just 6.3% leads to significantly different values in crop self-sufficiency, even with no dam developments. One of the most significant findings is that while the introduction of the hydropower dam does not seem to have a fundamental impact on crop self-sufficiency in absolute terms (Fig. 3), it does reduce resilience of crop self-sufficiency to climate change even with mitigation or adaptation interventions. This becomes particularly evident when elasticity drops from around 86% to 43% for the dam scenario, and lower again with alternative cropping systems or e-flow standards (25%).
A second key result is the marked trade-off between income and energy self-sufficiency on the one hand and resilience for relative water consumption on the other hand with e-flow requirements (mitigation scenario). The elasticity of relative water consumption is considerably lower than in all other scenarios. Energy self-sufficiency resilience to climate change decreases in all scenarios with the dam, even assuming that some of the new power capacity created by the hydropower developments will be redirected to the local economy. The adaptation and mitigation scenarios increase the hardness of energy self-sufficiency compared to the baseline (with dam), but they decrease elasticity marginally. Fish self-sufficiency is not resilient at all to climate change. The absence of any hardness or elasticity values in the tables indicates that any disturbance beyond the reference value leads to a significant deviation from the reference runs without the indicators ever bouncing back to the reference values.
Crop self-sufficiency is not resilient to additional population growth (absence of hardness as well as elasticity values in Table 5). For our other indicators, a deviation between 7% and 9% from the reference assumptions is sufficient for the system to deviate from its reference behavior (hardness values). Table 5 does not show elasticity measures for population growth because once indicator values differed significantly from their reference values in the model, they never bounce back in the model runs. This is likely because of the path dependencies for local food supply, which is highly reliant on local environmental systems and productive capacities.
Additional investments in hydropower capacity, e.g., Sambor dam, have mixed resilience impacts (Table 6). On the one hand, they improve energy self-sufficiency and per capita income (Fig. 3) given model assumptions about local employment gains and energy contributions to the local economy. However, hardness values are missing for most indicators and there are no elasticity measures once additional dam investments are introduced in the model run. Once again, when indicator values deviate from their reference values, they never bounce back because the model can find no way of fully compensating the reductions in crop, fish, or energy self-sufficiency or per capita income introduced by additional dam investments over time. This finding supports recent research on ecological design options for Sambor dam (Wild et al. 2019). Fish self-sufficiency does not appear either, but for a somewhat different reason. This indicator does not deviate significantly from scenario runs because there is little local fish production left to impact by the time the additional dam investment would be made. Similarly, a hardness value of 33% indicates that it does not take much more than current hydropower dam investment before crop self-sufficiency deteriorates even more.
Our aim here is to give insight into some advantages and disadvantages of our methodology. We note upfront that we cannot compare directly to other methods given unique contextual factors for our study. Instead, we reflect on the modeling process and outputs in the frame of our original intended objectives: to elicit and integrate knowledge from diverse stakeholders, assess direct and indirect, short- and long-term risks and consequences in the nexus situation in our Cambodia case and enhance agency for individuals and collectives who participated.
We did not depend on expert-led risk identification or enter through the nexus silos of water, energy, or food. Taking this systems perspective helped stakeholders share their own priority concerns, vulnerabilities, and knowledge in our process. The participatory CLDs surfaced unshared information and unaired assumptions, and this knowledge was reflected in the aggregated CLD and model structure (see Appendix 2 for further details). Because of this we gained a new understanding about how local communities are already trying to cope with severely degraded fish stocks even before hydropower development, for example. One local civil society partner remarked: “I myself learned that village people are really more concerned about illegal fishing. Their second priority is the [future] dam development. The illegal fishing is actually happening” (MSC3). Ideas and viewpoints that are not often raised were deliberated and negotiated in some groups where barriers to speaking across hierarchy were weakened, at least temporarily (see Bréthaut et al. 2019). Being able to talk about hydropower impacts with this mix of stakeholders was viewed as unusual and a successful outcome of the participatory modeling method by some project partners (MSC7) given political conditions.
The resulting scenario and resilience analysis produce some interesting insights for future risk governance in the landscape. They highlight how trade-offs between national energy infrastructure programs and local food security will likely be made in a situation where serious pressure is already being exerted on environmental systems in Kratie and Stung Treng provinces. This implies that although investing in fish management seems to be a robust strategy, it must be designed for conditions where dams are being developed on top of already degraded fish stocks. And, significantly, local food security problems could be triggered even by small increases in local population driven by construction activities, and not just by subsequent effects on fish production and other biodiversity of a completed and operational dam. Although climate change is a major concern for national government, Kratie and Stung Treng provinces may be more resilient to climate disturbances than to disturbances from population growth or additional dam investments.
Moreover, neither the adaptation or mitigation measures proposed by stakeholders can be relied on to fully compensate for loss in crop and fish self-sufficiency resulting from dam development under climate change conditions. This suggests giving weight to finding pathways to improved resilience outcomes. Two “no regrets” policy actions are worth further exploration in light of multiple and large uncertainties involved: (1) regenerating natural resources and strengthening local food production systems as a buffer. A focus on aquatic food production might be the best hedge for food security even with hydropower developments in the landscape, though this claim will have to be assessed against future increases in population and/or fishing effort; (2) requiring e-flow measures to be implemented by dam project developers with assessments of future possible water demand in the landscape. Effectiveness of e-flow measures will depend greatly on coordinated operational rules along the cascade of dams in the region and should not be expected to offset impacts completely. Our analysis suggests very careful consideration of assumptions related to three key variables in future research and governance actions in the MFF nexus:
The limitation of our analysis is that it stops at identifying the data essential to a full policy analysis and suggesting important factors to be understood before coming to policy conclusions. Nonetheless, stakeholder reflections on the scenario modeling results during final landscape and national-level workshops indicated that the procedure had helped develop provincial administration capacity for nexus analysis and governance. In a closing speech to the final provincial-level workshop, a senior provincial administration official reflected that, “The use of this information is easy because these findings have been obtained by all of you. We cannot take a U.S. study and adopt it here. Starting from the bottom up approach is easy ... because we can coordinate ... This study for all of us is unique. It brings to us the vision, one common vision for us. We can use it as a compass” (SF2.1_2016.07.19-20).
Interestingly, it was the developing planning processes that emerged as the most promising nexus governance opportunity by the end of our research process, not the expected sectoral nexus policy areas of water, energy, agricultural, and fisheries production. The Ministry of Interior D&D process is generating new guidelines for subnational development planning across Cambodia in the context of nation-wide development planning procedures, which in turn influence national sectoral strategies, like agriculture and fisheries management, climate action, and energy production. A number of partners and participants observed that current problem identification procedures in commune investment planning (typically SWOT analysis) is unhelpful because it generates narrow risk and priority assessments and actions (MSC1) compared to more holistic and integrated analysis enabled by the CLD process (MSC3). We initially provided training in the CLD method to our academic, civil society, and national government operational partners to be able to facilitate workshops in the Khmer language. After the project, the NCSD secretariat staff, WWF staff, RUPP and RUA academics all cited increases in their confidence and ability to facilitate basic CLD activities and train others to do the same (MSC3). Some key Ministry of Interior staff also showed interest in taking up the CLD methodology also, with one project partner reporting, “NCDD focal points in provincial administrations for the NCDD are really interested. They have already spent time to discuss where the tool could fit into the subnational development planning processes. I believe the NCDD could take up this this tool because they are currently in the process of updating their toolkit for commune investment planning. I have already received a call to invite me to present the CLD process at such an event.” (MSC4). Provincial administration staff were also interested in continuing with the CLD method but wanted to see the process formally integrated into national planning guidelines produced by the Ministry of Interior first (MSC12; see Bréthaut et al. 2019 for further details).
This outcome suggests something important for future applications of similar research and analysis methods. Originally, we anticipated that any new capacity for risk identification and management reported by partners would likely derive from new knowledge produced by the modeling analysis. In effect, we underestimated the impact of the CLD training provided to operational partners and the value derived by the participants from the CLD procedures themselves. We took up measurement of changed mental models (Scott et al. 2016) and enhanced agency for individuals and collectives in participatory system dynamics modeling in Kimmich et al. (2019) to study this question experimentally. Our findings reveal how participants in such processes can significantly change beliefs about likelihood of certain future events and their individual agency to manage these, while reducing some uncertainties within and across the groups about priority actions.
We share findings from the LIVES project on the use of one procedure for identifying key risks in one water-energy-food nexus case in the Mekong region. A motivating idea was that assessments should be carried out with stakeholders if such assessments are to support equitable risk allocation in situations of information asymmetries, low consensus on priorities and problems, and varying vulnerabilities and capacities.
Our chosen method was participatory system dynamics modeling with scenario and resilience analysis. Such models have been referred to as being theory-rich and data-poor (Pruyt 2014). We find this may be a strength of this method when it obliges nexus researchers to turn to local stakeholders as an important source of knowledge. Scenario analysis depends on so many empirical and structural modeling assumptions that always have to be made. In our case, we attempted to make these with stakeholders in a cocreated evaluative process with the result that risks are defined by those who might face them, and everyone’s assumptions and proffered responses are tested and validated by a collective process.
A lesson from our experience that reinforces the conclusions of other transdisciplinary research is that nexus research does not have to produce perfect data and incontestable results before helping to anticipate and identify responses to risks in the nexus. This case shows it is possible to use complex modeling approaches for stakeholder-led analyses. The process was time consuming for all involved, longer and more uncertain compared to more conventional approaches. However, stakeholders had an unexpected appetite for systems thinking precisely because they found it helpful in navigating the complexity of their situation. Participatory system dynamics models are currently underutilized in nexus research (Albrecht et al. 2018, Harwood 2018) but absolutely deserve further attention because of the opportunities for inclusion, deliberation, and learning they offer to nexus governance.
Finally, working with stakeholders and our analytical procedure underscored for us how nexus risk assessments change with changes in perspective. In light of this, we believe a tentative characterization of risk in the water-energy-food nexus will be helpful to future efforts in participatory modeling for such assessments. Risk intensification or transfer due to nexus relations is the probability and severity of consequences arising from rapidly changing framework conditions in systems affected by the dynamic interconnections between water, energy, and food production subsystems which, in turn, affect (1) hazard likelihood, exposure, and vulnerability (2) for multiple stakeholders (3) with varying adaptive capacities that have (4) different sensitivities to interventions (5) across scales. Even if ability to implement systems-thinking procedures is impaired, bearing in mind such characteristics supports a precautionary and multilevel approach in risk assessment for the nexus where social-ecological resilience is thought to be low.
We gratefully acknowledge funding support of the Nomis Foundation and the Mava Foundation. This research was undertaken as part of the Linked Indicators for Vital Ecosystem Services project (http://livesproject21.org/). Thank you to all participants and partners in our Cambodia activities. This article reflects the findings of the research but does not necessarily reflect the views of the institutions with which authors are affiliated.
The data that support the findings of this study are available on request from the corresponding author, [LG]. The data/code are not publicly available because they contain information that could compromise the privacy of research participants.
Al-Saidi, M., and N. A. Elagib. 2017. Towards understanding the integrative approach of the water, energy and food nexus. Science of The Total Environment 574:1131-1139. https://doi.org/10.1016/j.scitotenv.2016.09.046
Albrecht, T. R., A. Crootof, and C. A. Scott. 2018. The water-energy-food nexus: a systematic review of methods for nexus assessment. Environmental Research Letters 13(4):043002. https://doi.org/10.1088/1748-9326/aaa9c6
Babel, M. S., C. Nguyen Dinh, Md. Reaz Akter Mullick, and U. V. Nanduri. 2012. Operation of a hydropower system considering environmental flow requirements: a case study in La Nga River Basin, Vietnam. Journal of Hydro-Environment Research 6(1):63-73. https://doi.org/10.1016/j.jher.2011.05.006
Barlas, Y. 1996. Formal aspects of model validity and validation in system dynamics. System Dynamics Review 12(3):183-210. https://doi.org/10.1002/(SICI)1099-1727(199623)12:3<183::AID-SDR103>3.0.CO;2-4
Basco-Carrera, L., A. Warren, E. van Beek, A. Jonoski, and A. Giardino. 2017. Collaborative modelling or participatory modelling? A framework for water resources management. Environmental Modeling & Software 91(Supplement C):95-110. https://doi.org/10.1016/j.envsoft.2017.01.014
Bassi, A., L. Gallagher, and H. Helsingen. 2016. Green economy modelling of ecosystem services along the “Road to Dawei.” Environments 3(4):19. https://doi.org/10.3390/environments3030019
Bouckaert, F., Y. Wei, K. Hussey, J. Pittock, and R. Ison. 2018. Improving the role of river basin organisations in sustainable river basin governance by linking social institutional capacity and basin biophysical capacity. Current Opinion in Environmental Sustainability 33:70-79. https://doi.org/10.1016/j.cosust.2018.04.015
Boyd, E., B. Nykvist, S. Borgström, and I. A. Stacewicz. 2015. Anticipatory governance for social-ecological resilience. Ambio 44(1):149-161. https://doi.org/10.1007/s13280-014-0604-x
Bréthaut, C., L. Gallagher, J. Dalton, and J. Allouche. 2019. Power dynamics and integration in the water-energy-food nexus: learning lessons for transdisciplinary research in Cambodia. Environmental Science & Policy 94:153-162. https://doi.org/10.1016/j.envsci.2019.01.010
Bylander, M. 2015. Depending on the sky: environmental distress, migration, and coping in rural Cambodia. International Migration 53(5):135-147. https://doi.org/10.1111/imig.12087
Chapman, A., and S. Darby. 2016. Evaluating sustainable adaptation strategies for vulnerable mega-deltas using system dynamics modeling: rice agriculture in the Mekong Delta’s An Giang Province, Vietnam. Science of The Total Environment 559:326-338. https://doi.org/10.1016/j.scitotenv.2016.02.162
Clark, W. C., L. van Kerkhoff, L. Lebel, and G. C. Gallopin. 2016. Crafting usable knowledge for sustainable development. Proceedings of the National Academy of Sciences 113(17):4570-4578. https://doi.org/10.1073/pnas.1601266113
Dang, T. D., T. A. Cochrane, M. E. Arias, P. D. T. Van, and T. T. de Vries. 2016. Hydrological alterations from water infrastructure development in the Mekong floodplains. Hydrological Processes 30(21):3824-3838. https://doi.org/10.1002/hyp.10894
Dart, J., and R. Davies. 2003. A dialogical, story-based evaluation tool: the most significant change technique. American Journal of Evaluation 24(2):137-155. https://doi.org/10.1177/109821400302400202
Foran, T. 2015. Node and regime: interdisciplinary analysis of water-energy-food nexus in the Mekong region. Water Alternatives 8(1):655-674. [online] URL: http://www.water-alternatives.org/index.php/alldoc/articles/vol8/v8issue1/270-a8-1-3/file
Foran, T., J. Ward, E. J. Kemp-Benedict, and A. Smajgl. 2013. Developing detailed foresight narratives: a participatory technique from the Mekong region. Ecology and Society 18(4):6. https://doi.org/10.5751/ES-05796-180406
Forrester, J. W. 1961. Industrial dynamics. Productivity Press, Portland, Oregon, USA.
Fox, C., and C. Sneddon. 2019. Political borders, epistemological boundaries, and contested knowledges: constructing dams and narratives in the Mekong River basin. Water 11(3):413. https://doi.org/10.3390/w11030413
Gallagher, L., J. Dalton, C. Bréthaut, T. Allan, H. Bellfield, D. Crilly, K. Cross, D. Gyawali, D. Klein, S. Laine, X. LeFlaive, L. Li, A. Lipponen, N. Matthews, S. Orr, J. Pittock, C. Ringler, M. Smith, D. Tickner, U. von Schlippenbach, and F. Vuille. 2016. The critical role of risk in setting directions for water, food and energy policy and research. Current Opinion in Environmental Sustainability 23:12-16. https://doi.org/10.1016/j.cosust.2016.10.002
Gerritsen, A. L., M. Stuiver, and C. J. A. M Termeer. 2013. Knowledge governance: an exploration of principles, impact, and barriers. Science and Public Policy 40(5):604-615. https://doi.org/10.1093/scipol/sct012
Ghaffarzadegan, N., J. Lyneis, and G. P. Richardson. 2010. How small system dynamics models can help the public policy process. System Dynamics Review 27(1):22-44. https://doi.org/10.1002/sdr.442
Grafton, R. Q., M. McLindin, K. Hussey, P. Wyrwoll, D. Wichelns, C. Ringler, D. Garrick J. Pittock, S. Wheeler, S. Orr, N. Matthews, E. Ansink, A. Aureli, D. Connell, L. De Stefano, K. Dowsley, S. Farolfi, J. Hall, P. Katic, B. Lankford, H. Leckie, M. McCartney, H. Pohlner, N. Ratna, M. H. Rubarenzya, S. Narayan Sai Raman, K. Wheeler, and J. Williams. 2016. Responding to global challenges in food, energy, environment and water: risks and options assessment for decision-making. Asia & the Pacific Policy Studies 3(2):275-299. https://doi.org/10.1002/app5.128
Guston, D. H. 2014. Understanding “anticipatory governance.” Social Studies of Science 44(2):218-242. https://doi.org/10.1177/0306312713508669
Hagemann, N., and S. Kirschke. 2017. Key issues of interdisciplinary nexus governance analysis: lessons learned from research on integrated water resources management. Resources 6(1):9. https://doi.org/10.3390/resources6010009
Haimes, Y. Y. 2009. On the complex definition of risk: a systems-based approach. Risk Analysis 29(12):1647-1654. https://doi.org/10.1111/j.1539-6924.2009.01310.x
Harwood, S. A. 2018. In search of a (WEF) nexus approach. Environmental Science & Policy 83:79-85. https://doi.org/10.1016/j.envsci.2018.01.020
Herrera, H. 2017. From metaphor to practice: operationalizing the analysis of resilience using system dynamics modeling. Systems Research and Behavioral Science 34(4):444-462. https://doi.org/10.1002/sres.2468
Hoff, H. 2011. Understanding the nexus. Background paper for the Bonn2011 conference: the water, energy and food security nexus. Stockholm Environment Institute, Stockholm, Sweden. [online] URL: https://www.sei.org/publications/understanding-the-nexus/.
Hovmand, P. S., D. F. Andersen, E. Rouwette, G. P. Richardson, K. Rux, and A. Calhoun. 2012. Group model-building ‘scripts’ as a collaborative planning tool. Systems Research and Behavioral Science 29(2):179-193. https://doi.org/10.1002/sres.2105
Howarth, C., and I. Monasterolo. 2017. Opportunities for knowledge co-production across the energy-food-water nexus: making interdisciplinary approaches work for better climate decision making. Environmental Science & Policy 75:103-110. https://doi.org/10.1016/j.envsci.2017.05.019
Innes, J. E., and D. E. Booher. 2010. Planning with complexity: an introduction to collaborative rationality for public policy. Routledge, London, UK. https://doi.org/10.4324/9780203864302
International Renewable Energy Agency (IRENA). 2018. Renewable energy market analysis: Southeast Asia. IRENA, Abu Dhabi, UAE. [online] URL: https://irena.org/-/media/Files/IRENA/Agency/Publication/2018/Jan/IRENA_Market_Southeast_Asia_2018.pdf
Johnston, R., J. Cools, S. Liersch, S. Morardet, C. Murgue, M. Mahieu, I. Zsuffa, and G. P. Uyttendaele. 2013. WETwin: a structured approach to evaluating wetland management options in data-poor contexts. Environmental Science and Policy 34:3-17. https://doi.org/10.1016/j.envsci.2012.12.006
Jordan, R., S. Gray, M. Zellner, P. D. Glynn, A. Voinov, B. Hedelin, E. J. Sterling, K. Leong, L. Schmitt Olabisi, K. Hubacek, P. Bommel, T. K. BenDor, A. J. Jetter, B. Laursen, A. Singer, P. J. Giabbanelli, N. Kolagani L. Basco Carrera, K. Jenni, and C. Prell. 2018. Twelve questions for the participatory modeling community. Earth’s Future 6(8):1046-1057. https://doi.org/10.1029/2018EF000841
Khandker, R. S., D. F. Barnes, and H. A. Samad. 2013. Welfare impacts of rural electrification: a panel data analysis from Vietnam. Economic Development and Cultural Change 61(3):659-692. https://doi.org/10.1086/669262
Kimmich, C., L. Gallagher, B. Kopainsky, M. Dubois, C. Sovann, C. Buth, and C. Bréthaut. 2019. Participatory modeling updates expectations for individuals and groups, catalyzing behavior change and collective action in water-energy-food nexus governance. Earth’s Future 7(12):1337-1352. [online] URL: https://doi.org/10.1029/2019EF001311
Klinke, A., and O. Renn. 2012. Adaptive and integrative governance on risk and uncertainty. Journal of Risk Research 15(3):273-292. https://doi.org/10.1080/13669877.2011.636838
Kopainsky, B., G. Hager, H. Herrera, and P. H. Nyanga. 2017. Transforming food systems at local levels: using participatory system dynamics in an interactive manner to refine small-scale farmers’ mental models. Ecological Modeling 362:101-110. https://doi.org/10.1016/j.ecolmodel.2017.08.010
Lang, D. J., A., Wiek, M., Bergmann, M. Stauffacher, P. Martens, P. Moll, M. Swilling, and C. J. Thomas. 2012. Transdisciplinary research in sustainability science: practice, principles, and challenges. Sustainability Science 7(1):25-43. https://doi.org/10.1007/s11625-011-0149-x
Larcom, S., and T. van Gevelt. 2017. Regulating the water-energy-food nexus: interdependencies, transaction costs and procedural justice. Environmental Science and Policy 72:55-64. https://doi.org/10.1016/j.envsci.2017.03.003
Lebel, L., and B. Lebel. 2018. Nexus narratives and resource insecurities in the Mekong region. Environmental Science & Policy 90:164-172. https://doi.org/10.1016/j.envsci.2017.08.015
Luna-Reyes, L. F., I. J. Martinez-Moyano, T. A. Pardo, A. M. Cresswell, D. F. Andersen, and G. P. Richardson. 2006. Anatomy of a group model-building intervention: building dynamic theory from case study research. System Dynamics Review 22(4):291-320. https://doi.org/10.1002/sdr.349
Ly, P., L. S. Jensen, T. B. Bruun, and A. de Neergaard. 2016. Factors explaining variability in rice yields in a rain-fed lowland rice ecosystem in southern Cambodia. NJAS - Wageningen Journal of Life Sciences 78:129-137. https://doi.org/10.1016/j.njas.2016.05.003
Ly, P., L. S. Jensen, T. B. Bruun, D. Rutz, and A. de Neergaard. 2012. The system of rice intensification: adapted practices, reported outcomes and their relevance in Cambodia. Agricultural Systems 113:16-27. https://doi.org/10.1016/j.agsy.2012.07.005
Mekong River Commission (MRC). 2017. The study on the sustainable management and development of the Mekong River Basin, including impacts of mainstream hydropower projects. MRC, Vientiane, Laos. [online] URL: http://www.mrcmekong.org/assets/Publications/Council-Study/Council-study-Reports-discipline/Council-Study-BioRA-Final-Technical-Report-VOLUME-4-Development-Scenarios-FINAL-REPORT-DEC-2017.pdf
Mekong Wetlands Biodiversity Conservation and Sustainable Use Programme. 2005. Vulnerability assessment of Climate Risks in Stung Treng Province, Cambodia. UNDP, MRC, GEF, IUCN, Gland, Switzerland. [online] URL: https://www.iucn.org/content/vulnerability-assessment-climate-risks-stung-treng-province-cambodia
Middleton, C., N. Matthews, and N. Mirumachi. 2015. Whose risky business? Public-private partnerships (PPP), build-operate-transfer (BOT) and large hydropower dams in the Mekong region. Pages 224-237 in N. Matthews and K. Geheb, editors. Hydropower development in the Mekong region: political, socio-economic and environmental perspectives Earthscan, London, UK.
Milne, S., and S. Mahanty, editors. 2015. Conservation and development in Cambodia: exploring frontiers of change in nature, state and society. Routledge, London, UK. https://doi.org/10.4324/9781315887302
Mochizuki, J., S. Vitoontus, B. Wickramarachchi, S. Hochrainer-Stigler, K. Williges, R. Mechler, and R. Sovann. 2015. Operationalizing iterative risk management under limited information: fiscal and economic risks due to natural disasters in Cambodia. International Journal of Disaster Risk Science 6(4):321-334. https://doi.org/10.1007/s13753-015-0069-y
Molle, F., T. Foran, and M. Käkönen, editors. 2012. Contested waterscapes in the Mekong Region: hydropower, livelihoods and governance. Routledge, London, UK. https://doi.org/10.4324/9781849770866
Orr, S., J. Pittock, A. Chapagain, and D. Dumaresq. 2012. Dams on the Mekong River: lost fish protein and the implications for land and water resources. Global Environmental Change 22(4):925-932. https://doi.org/10.1016/j.gloenvcha.2012.06.002
Pahl-Wostl, C. 2019. The role of governance modes and meta-governance in the transformation towards sustainable water governance. Environmental Science and Policy 91:6-16. https://doi.org/10.1016/j.envsci.2018.10.008
Parkhurst, J. 2016. Appeals to evidence for the resolution of wicked problems: the origins and mechanisms of evidentiary bias. Policy Sciences 49(4):373-393. https://doi.org/10.1007/s11077-016-9263-z
Pittock, J., D. Dumaresq, and A. M. Bassi. 2016. Modeling the hydropower-food nexus in large river basins: a Mekong case study. Water 8:425. https://doi.org/10.3390/w8100425
Poff, N. L., and J. H. Matthews. 2013. Environmental flows in the Anthropocene: past progress and future prospects. Current Opinion in Environmental Sustainability 5(6):667-675. https://doi.org/10.1016/j.cosust.2013.11.006
Pohl, C., S. Rist, A. Zimmermann, P. Fry, G. S. Gurung, F. Schneider, C. Ifejika Speranza, B. Kiteme, S. Boillat, E. Serrano, G. Hirsch Hadorn, and U. Wiesmann. 2010. Researchers’ roles in knowledge co-production: experience from sustainability research in Kenya, Switzerland, Bolivia and Nepal. Science and Public Policy 37(4):267-281. https://doi.org/10.3152/030234210X496628
Pruyt, E. 2014. From data-poor to data-rich. System dynamics in the era of big data. Paper presented at the 32nd International Conference of the System Dynamics Society, 20-24 July. Delft, The Netherlands. [online] URL: https://proceedings.systemdynamics.org/2014/proceed/papers/P1390.pdf
Ratcliffe, R. 2020. Cambodia scraps plans for Mekong hydropower dams. The Guardian, 20 March. [online] URL: https://www.theguardian.com/world/2020/mar/20/cambodia-scraps-plans-for-mekong-hydropower-dams
Richter, B. D., and G. A. Thomas. 2007. Restoring environmental flows by modifying dam operations. Ecology and Society 12(1):12. https://doi.org/10.5751/ES-02014-120112
Rijke, J., R. Brown, C. Zevenbergen, R. Ashley, M. Farrelly, P. Morison, and S. van Herk. 2012. Fit-for-purpose governance: a framework to make adaptive governance operational. Environmental Science & Policy 22:73-84. https://doi.org/10.1016/j.envsci.2012.06.010
Rouwette, E. A. J. A. 2016. The impact of group model building on behavior. Pages 213-241 in M. Kunc, J. Malpass, and L. White, editors. Behavioral operational research: theory, methodology and practice. Palgrave Macmillan, London, UK. https://doi.org/10.1057/978-1-137-53551-1_11
Royal Government of Cambodia (RGC). 2008. The Rectangular Strategy for Growth, Employment, Equity, and Efficiency in Cambodia. Ministry of Planning. RGC, Phnom Penh, Cambodia.
Royal Government of Cambodia (RGC). 2011a. Kratie provincial development planning 2011-2015. Kratie provincial administration unit, RGC, Kratie, Cambodia.
Royal Government of Cambodia (RGC). 2011b. Stung Treng provincial development planning 2011-2015. Stung Treng provincial administration unit, RGC, Stung Treng, Cambodia.
Royal Government of Cambodia (RGC). 2014. National Strategic Development Plan 2014-2018. Ministry of Planning. RGC, Phnom Penh, Cambodia.
Royal Government of Cambodia (RGC). 2016a. DRAFT Cambodia Energy Sector Strategy. Ministry of Industry, Mines and Energy. RGC, Phnom Penh, Cambodia.
Royal Government of Cambodia (RGC). 2016b. Cambodia National Energy Statistics. Ministry of Industry, Mines and Energy. RGC, Phnom Penh, Cambodia.
Royal Government of Cambodia (RGC). 2016c. DRAFT Cambodia Power Development Plan. Ministry of Industry, Mines and Energy. RGC, Phnom Penh, Cambodia.
Royal Government of Cambodia (RGC). 2016d. National Climate Change Action Plan. Ministry of Environment. RGC, Phnom Penh, Cambodia.
Scheffer, M., S. R. Carpenter, T. M. Lenton, J. Bascompte, W. Brock, V. Dakos, J. van de Koppel, I. A. van de Leemput, S. A. Levin, E. H. van Nes, M. Pascual, and J. Vandermeer. 2012. Anticipating critical transitions. Science 338(6105):344-348. https://doi.org/10.1126/science.1225244
Schipanski, M. E., G. K. MacDonald, S. Rosenzweig, M. J. Chappell, E. M. Bennett, R. Bezner Kerr, J. Blesh, T. Crews, L. Drinkwater, J. G. Lundgren, and C. Schnarr. 2016. Realizing resilient food systems. BioScience 66(7):600-610. https://doi.org/10.1093/biosci/biw052
Scott, R., R. Y. Cavana, and D. Cameron. 2016. Mechanisms for understanding mental model change in group model building. Systems Research and Behavioral Science 33(1):100-118. https://doi.org/10.1002/sres.2303
Shannak, S., D. Mabrey, and M. Vittorio. 2018. Moving from theory to practice in the water-energy-food nexus: an evaluation of existing models and frameworks. Water-Energy Nexus 1(1):17-25. https://doi.org/10.1016/j.wen.2018.04.001
Siciliano, G., F. Urban, S. Kim, and P. D. Lonn. 2015. Hydropower, social priorities and the rural-urban development divide: the case of large dams in Cambodia. Energy Policy 86:273-285. https://doi.org/10.1016/j.enpol.2015.07.009
Sithirith, M. 2016. Dams and state security: damming the 3S rivers as a threat to Cambodian state security. Asia Pacific Viewpoint 57(1):60-75. https://doi.org/10.1111/apv.12108
Smajgl, A., and J. Ward. 2013. The water-food-energy nexus in the Mekong region: assessing development strategies considering cross-sectoral and transboundary impacts. Springer, New York, New York, USA. https://doi.org/10.1007/978-1-4614-6120-3
Smajgl, A., J. R. Ward, T. Foran, J. Dore, J. Ward, and S. Larson. 2015. Visions, beliefs, and transformation: exploring cross-sector and transboundary dynamics in the wider Mekong region. Ecology and Society 20(2):15. https://doi.org/10.5751/ES-07421-200215
Smajgl, A., J. Ward, and L. Pluschke. 2016. The water-food-energy nexus - realising a new paradigm. Journal of Hydrology 533:533-540. https://doi.org/10.1016/j.jhydrol.2015.12.033
Sterman, J. 2000. Business dynamics: systems thinking and modeling for a complex world. Irwin/McGraw-Hill, New York, New York, USA.
Thilakarathne, M., and V. Sridhar. 2017. Characterization of future drought conditions in the Lower Mekong river basin. Weather and Climate Extremes 17:47-58. https://doi.org/10.1016/j.wace.2017.07.004
Thomas, T. S., T. Ponlok, R. Bansok, T. De Lopez, C. Chiang, N. Phirun, and C. Chhun. 2013. Cambodian agriculture: adaptation to climate change impact. IFPRI Discussion Paper 01285. International Food Policy Research Institute, Washington D.C., USA [online] URL: https://reliefweb.int/sites/reliefweb.int/files/resources/Cambodian%20agriculture%20adaptation%20to%20climate%20change%20impact.pdf
Thompson, J. R., C. L. R. Laizé, A. J. Green, M. C. Acreman, and D. G. Kingston. 2014. Climate change uncertainty in environmental flows for the Mekong River. Hydrological Sciences Journal 59(3-4):935-954. [online] URL: https://doi.org/10.1080/02626667.2013.842074
UN FAOSTAT 2018. Crops. Food and Agriculture Organization of United Nations, Rome, Italy. [online] URL: http://faostat.fao.org/site/567/DesktopDefault.aspx?PageID=567#ancor
Urban, F., G. Siciliano, K. Sour, P. D. Lonn, M. Tan-Mullins, and G. Mang. 2015. South-south technology transfer of low-carbon innovation: large Chinese hydropower dams in Cambodia. Sustainable Development 23(4):232-244. https://doi.org/10.1002/sd.1590
van der Voorn, T., J. Quist, C. Pahl-Wostl, and M. Haasnoot. 2017. Envisioning robust climate change adaptation futures for coastal regions: a comparative evaluation of cases in three continents. Mitigation and Adaptation Strategies for Global Change 22(3):519-546. https://doi.org/10.1007/s11027-015-9686-4
Videira, N., P. Antunes, R. Santos, and R. Lopes. 2010. A participatory modeling approach to support integrated sustainability assessment processes. Systems Research and Behavioral Science 27(4):446-460. https://doi.org/10.1002/sres.1041
Villamayor-Tomas, S., M. Avagyan, M. Firlus, G. Helbing, and M. Kabakova. 2016. Hydropower vs. fisheries conservation: a test of institutional design principles for common-pool resource management in the lower Mekong basin social-ecological system. Ecology and Society 21(1):3. https://doi.org/10.5751/es-08105-210103
Voinov, A., N. Kolagani, M. K. McCall, P. D. Glynn, M. E. Kragt, F. O. Ostermann, S. Pierce, and P. Ramu. 2016. Modeling with stakeholders - next generation. Environmental Modelling & Software 77:196-220. https://doi.org/10.1016/j.envsoft.2015.11.016
Vuković, D., and M. Babović. 2018. The trap of neo-patrimonialism: social accountability and good governance in Cambodia. Asian Studies Review 42(1):144-160. https://doi.org/10.1080/10357823.2017.1414773
Walker, B., C. S. Holling, S. R. Carpenter, and A. Kinzig. 2004. Resilience, adaptability and transformability in social-ecological systems. Ecology and Society 9(2):5. https://doi.org/10.5751/ES-00650-090205
Weber, E. U. 1997. The utility of measuring and modeling perceived risk. Pages 45-57 in A. A. J. Marley, editor. Choice, decision, and measurement: essays in honor of R. Duncan Luce. First edition. Psychology Press, Routledge, New York, USA. https://doi.org/10.4324/9781315789408-4
Weitz, N., C. Strambo, E. Kemp-Benedict, and M. Nilsson. 2017. Closing the governance gaps in the water-energy-food nexus: insights from integrative governance. Global Environmental Change 45:165-173. https://doi.org/10.1016/j.gloenvcha.2017.06.006
Wild, T. B., P. M. Reed, D. P. Loucks, M. Mallen-Cooper, and E. D. Jensen. 2019. Balancing hydropower development and ecological impacts in the Mekong: tradeoffs for Sambor mega dam. Journal of Water Resources Planning and Management 145(2):05018019. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001036
World Bank and The Asia Foundation. 2013. Voice, choice and decision: a study of local governance processes in Cambodia. World Bank, Washington, D.C., USA. [online] URL: https://openknowledge.worldbank.org/handle/10986/16758
Wu, S., H. Ishidaira, and W. Sun. 2010. Potential impact of Sambor dam project on interaction between Mekong river and Tonle Sap lake. Annual Journal of Hydraulic Engineering 54:109-114. [online] URL: http://library.jsce.or.jp/jsce/open/00028/2010/54-0019.pdf
Yung, L., E. Louder, L. Gallagher, K. Jones, and C. Wyborn. 2019. How methods for navigating uncertainty connect science and policy at the water-energy-food nexus. Frontiers in Environmental Science: Freshwater Science. https://doi.org/10.3389/fenvs.2019.00037