Cockerill, K., M. Armstrong, J. Richter, J. Okie. 2017. Environmental Realism: Challenging Solutions. Palgrave MacMillan 145p. ISBN 978-3-319-52824-3.
Abstract from Chapter 1: Why Challenge Solutions?
Labeling a problem ‘environmental’ creates a pervasive belief that science and technology can, should, and will generate solutions for issues ranging from pandemic disease to stream functions to nuclear contamination. These, however, are ‘wicked problems’ that defy simple or long-term solutions, but rather must be continually managed. Further, what are defined in the 21st century as ‘environmental problems’ are often the consequence of perceived ‘solutions’ implemented in a previous era.The perception of these issues as problems is derived, in part, from Enlightenment ideas segregating Homo sapiens from nature and a belief that humans can contain or control biophysical processes. Solutionist thinking and language perpetuates a self-referential problem-solution-problem cycle that begs the question of what constitutes a ‘solution’ and simultaneously elides the reality that human systems and biophysical systems are inseparable.
Shortridge, J., Guikema, S. & Zaitchik, B. (2017) Robust decision making in data scarce contexts: addressing data and model limitations for infrastructure planning under transient climate change. Climatic Change 140: 323. doi:10.1007/s10584-016-1845-4
Abstract: In the face of deeply uncertain climate change projections, robust decision frameworks are becoming a popular tool for incorporating climate change uncertainty into water infrastructure planning. These methodologies have the potential to be particularly valuable in developing countries where extensive infrastructure development is still needed and uncertainties can be large. However, many applications of these methodologies have relied on a sophisticated process of climate model downscaling and impact modeling that may be unreliable in data-scarce contexts. In this study, we demonstrate a modified application of the robust decision making (RDM) methodology that is specifically tailored for application in data-scarce situations. This modification includes a novel method for generating transient climate change sequences that account for potential variable dependence but do not rely on detailed GCM projections, and an emphasis on identifying the relative importance of data limitations and uncertainty within an integrated modeling framework. We demonstrate this methodology in the Lake Tana basin in Ethiopia, showing how the approach can highlight the vulnerability of alternative plans across different time scales and identify priorities for research and model refinement. We find that infrastructure performance is particularly sensitive to uncertainty in streamflow model accuracy, irrigation efficiency, and evaporation rates, suggesting that additional research in these areas could provide valuable insights for long-term infrastructure planning. This work demonstrates how tailored application of robust decision frameworks using simple modeling approaches can provide decision support in data-scarce regions where more complex modeling and analysis may be impractical.
Gong, Min , Robert Lempert, Andrew M Parker, Lauren A. Mayer, Jordan Fischbach, Matthew Sisco, Zhamin Mao, David H. Krantz, and Howard Kunreuther. “Testing the Scenario Hypothesis: An Experimental Comparison of Scenarios and Forecasts for Decision Support in a Complex Decision Environment.” Environmental Modeling and Software 91 (2017): 135-55.
Decision support tools are known to influence and facilitate decisionmaking through the thoughtful construction of the decision environment. However, little research has empirically evaluated the effects of using scenarios and forecasts. In this research, we asked participants to recommend a fisheries management strategy that achieved multiple objectives in the face of significant uncertainty. A decision support tool with one of two conditions—Scenario or Forecast—encouraged participants to explore a large set of diversified decision options. We found that participants in the two conditions explored the options similarly, but chose differently. Participants in the Scenario Condition chose the strategies that performed well over the full range of uncertainties (robust strategies) significantly more frequently than did those in the Forecast Condition. This difference seems largely to be because participants in the Scenario Condition paid increased attention to worst-case futures. The results offer lessons for designing decision support tools.
Lahtinen, T. J., J. H. A. Guillaume, and R. P. Hämäläinen (2017), Why pay attention to paths in the practice of environmental modelling?, Environmental Modelling and Software, 92, 74–81, http://dx.doi.org/10.1016/j.envsoft.2017.02.019
Taking the ‘path perspective’ helps to understand and improve the practice of environmental modelling and decision making. A path is the sequence of steps taken in a modelling project. The problem solving team faces several forks where alternative choices can be made. These choices determine the path, together with the impact of uncertainties and exogenous effects. This paper discusses phenomena that influence the problem solvers’ choices at the forks. Situations are described where it can be desirable to re-direct the path or backtrack on it. Phenomena are identified that can cause the modelling project to get stuck on a poor path. The concept of a path draws attention to the interplay of behavioral phenomena and the sequential nature of modelling processes. This helps understand the overall effect of the behavioral phenomena. A path checklist is developed to help practitioners detect forks and reflect on the path of the modelling project.
Derbyshire, J. and Giovannetti, E. (2017) Understanding the failure to understand New Product Development failures: Mitigating the uncertainty associated with innovating new products by combining scenario planning and forecasting, Technological Forecasting & Social Change: http://www.sciencedirect.com/science/article/pii/S0040162516302980
In this paper we show that New Product Development (NPD) is subject to fundamental uncertainty that is both epistemic and ontic in nature. We argue that this uncertainty cannot be mitigated using forecasting techniques exclusively, because these are most useful in circumstances characteristic of probabilistic risk, as distinct from non-probabilistic uncertainty. We show that the mitigation of uncertainty in relation to NPD requires techniques able to take account of the socio-economic factors that can combine to cause present assumptions about future demand conditions to be incorrect. This can be achieved through an Intuitive Logics (IL) scenario planning process designed specifically to mitigate uncertainty associated with NPD by incorporating insights from both quantitative modelling alongside consideration of political, social, technological and legal factors, as-well-as stakeholder motivations that are central to successful NPD. In this paper we therefore achieve three objectives: 1) identify the aspects of the current IL process salient to mitigating the uncertainty of NPD; 2) show how advances in diffusion modelling can be used to identify the social-network and contagion effects that lead to a product’s full diffusion; and 3) show how the IL process can be further enhanced to facilitate detailed consideration of the factors enabling and inhibiting initial market-acceptance, and then the forecasted full diffusion of a considered new product. We provide a step-by-step guide to the implementation of this adapted IL scenario planning process designed specifically to mitigate uncertainty in relation to NPD.
Almeida, S., Holcombe, E. A., Pianosi, F. and Wagener, T. (2017). Dealing with deep uncertainties in landslide modelling for disaster risk reduction under climate change, Nat. Hazards Earth Syst. Sci., 17, 225-241, doi:10.5194/nhess-17-225-2017.
Landslides have large negative economic and societal impacts, including loss of life and damage to infrastructure. Slope stability assessment is a vital tool for landslide risk management, but high levels of uncertainty often challenge its usefulness. Uncertainties are associated with the numerical model used to assess slope stability and its parameters, with the data characterizing the geometric, geotechnic and hydrologic properties of the slope, and with hazard triggers (e.g. rainfall). Uncertainties associated with many of these factors are also likely to be exacerbated further by future climatic and socio-economic changes, such as increased urbanization and resultant land use change. In this study, we illustrate how numerical models can be used to explore the uncertain factors that influence potential future landslide hazard using a bottom-up strategy. Specifically, we link the Combined Hydrology And Stability Model (CHASM) with sensitivity analysis and Classification And Regression Trees (CART) to identify critical thresholds in slope properties and climatic (rainfall) drivers that lead to slope failure. We apply our approach to a slope in the Caribbean, an area that is naturally susceptible to landslides due to a combination of high rainfall rates, steep slopes, and highly weathered residual soils. For this particular slope, we find that uncertainties regarding some slope properties (namely thickness and effective cohesion of topsoil) are as important as the uncertainties related to future rainfall conditions. Furthermore, we show that 89 % of the expected behaviour of the studied slope can be characterized based on only two variables – the ratio of topsoil thickness to cohesion and the ratio of rainfall intensity to duration.
Kingsborough, Jenkins & Hall, Development and appraisal of long-term adaptation pathways for managing heat-risk in London, Climate Risk Management, 2017, http://dx.doi.org/10.1016/j.crm.2017.01.001
The risk of residential overheating and mortality is increasing due to the effects of global warming and the urban heat island effect and needs to be addressed through climate change adaptation. ‘Adaptation pathways’ have become widely recognised as an adaptation planning approach, but they have not been utilised for long-term planning for city-scale urban heat risk management. This paper applies adaptation pathway methodology to urban heat risk management. We use spatially coherent downscaled probabilistic climate change projections that account for changes in urban-land cover and the urban heat island to appraise adaptation pathways and inform long-term adaptation planning. We demonstrate that adaptation strategies focusing solely on urban greening or building level adaptation based on current best practice are unlikely to cope with the increasing levels of risk. Air-conditioning may play a growing role in managing heat-risk; however, increasing air-conditioning will exacerbate the urban heat island and further increase the risks of overheating.
Malekpour, S., Brown, R. R., de Haan, F. J., & Wong, T. H. F. (2017). Preparing for disruptions: A diagnostic strategic planning intervention for sustainable development. Cities, 63, 58–69. http://doi.org/10.1016/j.cities.2016.12.016
Despite the emphasis on sustainable development in some of the contemporary planning and policy rhetoric, we face an implementation deficit in practice. The impediments to the widespread adoption and successful implementation of sustainable infrastructure in cities’ critical sectors—such as water, energy or transport—are varied and complex. Although the scholarship has made some attempts to understand and categorize those impediments, not much has been said about how to identify them in a specific practical context. This study proposes a model for a diagnostic intervention in the ongoing process of strategic infrastructure planning, as a way of revealing context-specific impediments. The diagnostic intervention incorporates an explicit and reflexive consideration of short-term barriers and long-term disruptors into the strategic planning process, and assists with drafting the required coping strategies. The intervention has been tested in water infrastructure planning for one of the world’s largest urban renewal areas in Melbourne, Australia. This trial application provided promising outcomes for addressing the implementation deficit of sustainable development: it created a platform for various stakeholder groups to engage in explicit discussions on their confronted problems, which often have trans-organizational causes and impacts; it enabled reflexivity within the ongoing planning process; and, it helped to consider a large portfolio of future uncertainties to provide an enabling condition for more robust decisions to be made. Moreover, the trialed intervention provided empirical evidence in support of the scholarly discourse which contends that sustainable infrastructure delivery is not only about the development of technical solutions, but is also about the development of processes and tools that support the widespread adoption and successful implementation of those solutions in the face of wide-ranging impediments.
Hermans, Leon M., Marjolijn Haasnoot, Judith ter Maat, Jan H. Kwakkel. (2017). Designing monitoring arrangements for collaborative learning about adaptation pathways. Environmental Science & Policy 69: 29-38. DOI: 10.1016/j.envsci.2016.12.005 . https://authors.elsevier.com/a/1UFzx5Ce0rOGPN
Adaptation pathways approaches support long-term planning under uncertainty. The use of adaptation pathways implies a systematic monitoring effort to inform future adaptation decisions. Such monitoring should feed into a long-term collaborative learning process between multiple actors at various levels. This raises questions about who should monitor what, when and for whom. We formulate an approach that helps to address these questions, developed around the conceptual core offered by adaptive policy pathways methods and their notion of signposts and triggers. This is embedded in a wider approach that revisits the critical assumptions in underlying basic policies, looks forward to future adaptation decisions, and incorporates reciprocity in the organization of monitoring and evaluation. The usefulness and practical feasibility of the approach is studied for a case of the Delta Programme in the Netherlands, which incorporated adaptation pathways in its planning approach called adaptive delta management. The case results suggest that our approach adds value to existing monitoring practices. They further show that different types of signposts exist. Technical signposts, in particular, need to be distinguished from political ones, and require different learning processes with different types of actors.
- Lawrence, J., M. Haasnoot (2016) What it took to catalyse uptake of dynamic adaptive pathways planning to address climate change uncertainty, Environmental Science & Policy, Volume 68, February 2017, Pages 47-57, ISSN 1462-9011, http://dx.doi.org/10.1016/j.envsci.2016.12.003.
Implementing climate-resilient pathways in conditions of uncertainty and change is a serious challenge. Approaches have been developed for this type of problem, one of which, Dynamic Adaptive Policy Pathways approach (DAPP), has been applied in practice in a limited number of circumstances, mainly for large infrastructure projects and at national scales. To better understand what it takes to catalyse uptake of DAPP to better address uncertainty and change than typical static planning approaches, we examined the role of a simulation game facilitated by a knowledge broker, in a real-life local decision setting on flood risk management in New Zealand. Four intervention phases over four years are described and their influence analysed: 1) creating interest through framing the science, 2) increasing awareness using the Game, 3) experimenting with DAPP, and 4) uptake of DAPP. We found that a knowledge broker introducing new framing of changing risk profiles, facilitating use of the Game and the DAPP approach in a real-life decision making setting, with contextual support from events and (inter)national reports, catalysed the uptake of adaptive pathways planning. We identified enabling requirements necessary for embedding adaptive planning into decision-making practice for addressing uncertainty and change.
Keywords: Decision making; Deep uncertainty; Adaptation pathways; Climate change adaptation; Serious game; Flood risk management