Using Robust Decision Making to support the development of Dynamic Adaptive Policy Pathways and its associated monitoring system: A Helensville case-study

Abstract

Poor and coastal regions are increasingly at risk from the effects of climate change. These risks are accompanied by a high level of uncertainty, which is also called deep uncertainty. Current planning approaches lose efficacy under deep uncertainty, necessitating new approaches that function better under these conditions. Decision making under deep uncertainty (DMDU) is the name for the family of approaches that attempt to deal with this level of uncertainty. Bangladesh, the Netherlands, and New Zealand have all already adopted the use of DMDU techniques. Research into local implementation of these techniques is also being done.

Two DMDU techniques often deemed as complementary are robust decision making (RDM) and dynamic adaptive policy pathways (DAPP). RDM can be seen as a computational extension of scenario planning, where proposed plans are tested against every potential combination of uncertainties. DAPP is a flexible policy framework that allows decision-makers to keep long-term plans in mind while making short-term decisions. DAPP especially has seen increased adoption in national delta protection plans such as in the Netherlands, Bangladesh, and New Zealand.

To design DAPP, currently a combination of many-objective robust optimization (MORO) and participatory processes are used. These methods both have their own issues. MORO requires the upfront specification of rules and policies and is computationally expensive, while the participatory approach is qualitative and can be insufficient when dealing with complex systems. RDM is seen as a potential improvement in supporting the DAPP policy structure in two main ways. First, RDM can be used to iteratively develop and/or stress-test potential actions and pathways. Second, the vulnerabilities identified through RDM can be used to lay the base for a monitoring system by identifying promising signposts and signals.

While RDM is seen as a potentially helpful tool to support DAPP, there is a lack of studies that have established a systematic and analytical approach which uses the robust decision making process to support the development and monitoring of DAPP. This research proposes a novel approach based on literature to achieve this. The approach uses the vulnerabilities identified through RDM to iteratively inform and develop more robust actions and to lay the basis for the technical side of a monitoring system. This approach is then illustrated by way of the adaptation case of a wastewater treatment plant in Helensville, New Zealand. This wastewater treatment plant serves a small community and will have to retreat at some point in the future due to increasing risks from compound flooding, which are exacerbated by rising sea levels.

The results of the case illustration show the benefits of using RDM to better understand vulnerabilities in the system in two main ways. First, the vulnerability analysis (which included a sensitivity analysis) helped to identify factors most important to the outcomes to inform potentially effective actions. Second, RDM helped in the development of the monitoring system. Those factors making up the identified vulnerabilities formed the basis of the technical signposts selected. Using the coverage-density tradeoff from the scenario discovery results, promising signals could be selected, although timing was not taken into account. This could potentially partially solve a common problem for monitoring DAPP: the selection of trustworthy signals.

There were three main recommendations. The first is to further work through a case such as this, since due to time constraints only the first iteration of the process was followed in this research. This could help identify more potential benefits or issues. A main issue here is also how to identify when an action is fully developed, as the process could continue indefinitely. Second, it is recommended to do further research into determining adaptation tipping points using other scenario discovery methods, and to use the coverage-density tradeoff from the scenario discovery results to modify adaptation tipping points based on policy regret. Third, is to further the monitoring system by posing open questions to support the deliberation on signpost and signal selection, taking timing into account to identify triggers, and by adding a signpost map next to the signal map to visualize signpost interaction, hierarchy, and quality.