Gao, L., Bryan, B.A., 2017. Finding pathways to national-scale land-sector sustainability. Nature 544(7649) 217–222. https://www.nature.com/nature/journal/v544/n7649/abs/nature21694.html
The 17 Sustainable Development Goals (SDGs) and 169 targets under Agenda 2030 of the United Nations map a coherent global sustainability ambition at a level of detail general enough to garner consensus amongst nations. However, achieving the global agenda will depend heavily on successful national-scale implementation, which requires the development of effective science-driven targets tailored to specific national contexts and supported by strong national governance. Here we assess the feasibility of achieving multiple SDG targets at the national scale for the Australian land-sector. We scaled targets to three levels of ambition and two timeframes, then quantitatively explored the option space for target achievement under 648 plausible future environmental, socio-economic, technological and policy pathways using the Land-Use Trade-Offs (LUTO) integrated land systems model. We show that target achievement is very sensitive to global efforts to abate emissions, domestic land-use policy, productivity growth rate, and land-use change adoption behaviour and capacity constraints. Weaker target-setting ambition resulted in higher achievement but poorer sustainability outcomes. Accelerating land-use dynamics after 2030 changed the targets achieved by 2050, warranting a longer-term view and greater flexibility in sustainability implementation. Simultaneous achievement of multiple targets is rare owing to the complexity of sustainability target implementation and the pervasive trade-offs in resource-constrained land systems. Given that hard choices are needed, the land-sector must first address the essential food/fibre production, biodiversity and land degradation components of sustainability via specific policy pathways. It may also contribute to emissions abatement, water and energy targets by capitalizing on co-benefits. However, achieving targets relevant to the land-sector will also require substantial contributions from other sectors such as clean energy, food systems and water resource management. Nations require globally coordinated, national-scale, comprehensive, integrated, multi-sectoral analyses to support national target-setting that prioritizes efficient and effective sustainability interventions across societies, economies and environments.
McCurdy, A. and W. Travis (2017) Simulated climate adaptation in stormwater systems: evaluating the efficiency of adaptation strategies. Environment Systems and Decisions. Published online: http://link.springer.com/article/10.1007/s10669-017-9631-z
Adaptations in infrastructure may be necessitated by changes in temperature and precipitation patterns to avoid losses and maintain expected levels of service. A roster of adaptation strategies has emerged in the climate change literature, especially with regard to timing: anticipatory, concurrent, or reactive. Significant progress has been made in studying climate change adaptation decision making that incorporates uncertainty, but less work has examined how strategies interact with existing infrastructure characteristics to influence adaptability. We use a virtual testbed of highway drainage crossings configured with a selection of actual culvert emplacements in Colorado, USA, to examine the effect of adaptation strategy and culvert characteristics on cost efficiency and service level under varying rates of climate change. A meta-model approach with multinomial regression is used to compare the value of better climate change predictions with better knowledge of existing crossing characteristics. We find that, for a distributed system of infrastructural units like culverts, knowing more about existing characteristics can improve the efficacy of adaptation strategies more than better projections of climate change. Transportation departments choosing climate adaptation strategies often lack detailed data on culverts, and gathering that data could improve the efficiency of adaptation despite climate uncertainty.
Su H, Dong F, Liu Y, Zou R, and Guo H. 2017. Robustness-Optimality Tradeoff for Watershed Load Reduction Decision Making under Deep Uncertainty. Water Resour Manag:1-14.
Practical and optimal reduction of watershed loads under deep uncertainty requires sufficient search alternatives and direct evaluation of robustness. These requirements contribute to the understanding of the tradeoff between cost and robustness; while they are not well addressed in previous studies. This study thereby (a) uses preconditioning technique in Evolutionary Algorithm to reduce unnecessary search space, which enables a sufficient search; and (b) derives Robustness Index (RI) as a second-tier optimization objective function to achieve refined solutions (solved by GA) that address both robustness and optimality. Uncertainty-based Refined Risk Explicit Linear Interval Programming is used to generate alternatives (solved by Controlled elitist NSGA-II). The robustness calculation error is also quantified. Proposed approach is applied to Lake Dianchi, China. Results demonstrate obvious improvement in robustness after conducting sufficient search and negative robustness-optimality trade-offs, and provides a detailed characteristic of robustness that can serve as references for decision-making.
Robustness index Deep uncertainty Tradeoff Optimality Load reduction
Quinn, J. D., P. M. Reed and K. Keller (2017). “Direct policy search for robust multi-objective management of deeply uncertain socio-ecological tipping points.” Environmental Modelling & Software 92, http://dx.doi.org/10.1016/j.envsoft.2017.02.017: 125-141.
Managing socio-ecological systems is a challenge wrought by competing societal objectives, deep uncertainties, and potentially irreversible tipping points. A classic, didactic example is the shallow lake problem in which a hypothetical town situated on a lake must develop pollution control strategies to maximize its economic benefits while minimizing the probability of the lake crossing a critical phosphorus (P) threshold, above which it irreversibly transitions into a eutrophic state. Here, we explore the use of direct policy search (DPS) to design robust pollution control rules for the town that account for deeply uncertain system characteristics and conflicting objectives. The closed loop control formulation of DPS improves the quality and robustness of key management tradeoffs, while dramatically reducing the computational complexity of solving the multi-objective pollution control problem relative to open loop control strategies. These insights suggest DPS is a promising tool for managing socio-ecological systems with deeply uncertain tipping points.
Hamel, P. and BP Bryant (2017). Uncertainty assessment in ecosystem services analyses: Common challenges and practical responses. Ecosystem Services 24, 1-15. doi:10.1016/j.ecoser.2016.12.008
Abstract: Ecosystem services (ES) analyses are increasingly used to address societal challenges, but too often are not accompanied by uncertainty assessment. This omission limits the validity of their findings and may undermine the ‘science-based’ decisions they inform. We summarize and analyze seven commonly perceived challenges to conducting uncertainty assessment that help explain why it often receives superficial treatment in ES studies. We connect these challenges to solutions in relevant scientific literature and guidance documents. Since ES science is based on a multiplicity of disciplines (e.g. ecology, hydrology, economics, environmental modeling, policy sciences), substantial knowledge already exists to identify, quantify, and communicate uncertainties. The integration of these disciplines for solution-oriented modeling has been the focus of the integrated assessment community for many years, and we argue that many insights and best practices from this field can be directly used to improve ES assessments. We also recognize a number of issues that hinder the adoption of uncertainty assessment as part of standard practice. Our synthesis provides a starting point for ES analysts and other applied modelers looking for further guidance on uncertainty assessment and helps scientists and decision-makers to set reasonable expectations for characterizing the level of confidence associated with an ES assessment.
Readers interested in uncertainty and ES may also find a recent workshop report on “Motivating and Improving Uncertainty Assessment in ES” interesting as well:
Finally, those having or seeking to produce good examples of such assessment are encouraged to submit to a new special issue on the topic, with submissions due September 30, 2017.
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.
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.