@article {bnh-5263,
	title = {Strategic adaptation pathway planning to manage sea-level rise and changing coastal flood risk},
	journal = {Environmental Science \& Policy},
	volume = {87},
	year = {2018},
	month = {10/2018},
	pages = {9},
	chapter = {92},
	abstract = {<p><span>Communities around the world are already committed to future sea-level rise. Long-term adaptation planning to manage associated coastal flood impacts is, however, challenged by uncertainty and contested stakeholder priorities. This study provides a proof of concept for a combined robust decision making (RDM) and dynamic adaptive policy pathways (DAPP) approach in coastal flood risk management. The concept uses model-based support and largely open source tools to help local government plan coastal adaptation pathways. Key steps in the method are illustrated using a hypothetical case study in Australia. The study shows how scenario discovery can provide multi-dimensional descriptions of adaptation tipping points which may inform the development of technical signpost indicators. Transient scenarios uncovered limitations in seemingly robust adaptation policies, where historical path dependencies may constrain the rate of adaptation and the extent to which future coastal flood impacts can be successfully managed. Lived values have the potential to offer insights about non-material social trade-offs that residents may need to accept for the benefit of reduced flood risk, and could form a basis for defining socially-oriented signpost indicators. However, the nuances and subjectivity of lived values means that ongoing engagement with residents is essential as part of a combined RDM and DAPP approach to preserve the communities{\textquoteright} way of life. The learnings from this hypothetical case study suggest that testing in a real world participatory setting could be valuable in further developing a combined RDM and DAPP approach to plan adaptation pathways and manage future coastal flood risk.</span></p>},
	doi = {10.1016/j.envsci.2018.06.001},
	url = {https://www.sciencedirect.com/science/article/pii/S1462901118303678},
	author = {Timothy Ramm and Christopher Stephen Watson and Christopher J. White}
}
