Planning Support Systems for Long-Term Climate Resilience: A Critical Review

Krishnan, S., Aydin, N.Y. and Comes, M., 2021. Planning Support Systems for Long-Term Climate Resilience: A Critical Review. Urban Informatics and Future Cities, p.465.



Abstract:
As climate change is becoming a reality, there is an increasing demand to improve urban resilience. Planning Support Systems (PSS) enable climate-informed plann ing. However, previous research confirms difficulties in the uptake of PSS due to their resource-intensive nature and lack of awa eness of their usefulness. This chapter aims to make a headway in understanding research priorities and gaps that need to be addressed for PSS to address climate resilience in the long run. To this end, we review the emerging body of knowledge in academia and practice, by conducting a text-mining analysis of academic (n = 36,405) and non-academic (practice) (n = 86) literature on urban planning and climate resilience. We extract trends in climate pressures, infrastructure drivers, and planning approaches.

We use the #DPSIR framework to systematically analyse and compare key drivers, pressures and impacts, resilience characteristics and planning responses across an array of #urban applications. We then use term frequency to explore and compare the most important trends in academia and practice. Both corpora emphasize on #climate change, flood risk, population, and vulnerabilities but practice stresses also disaster risk, sea level rise and heat stress.  This results in an overview of the most important concepts under the four knowledge streams from the DPSIR framework.

A key finding from the academic literature is that long-term planning continues to be limited to a few fixed scenarios and places a strong focus on single sector strategies. Practice documents continue to be designed to inform high-level policies, but not spatial plans that require integrated thinking. Our analysis concludes with a research agenda for improving PSS to (1) identify and integrate the full range of variables in the long-term; (2) support selection of appropriate planning responses across multiple infrastructure systems; and (3) improve flexibility in planning by a deeper understanding of temporal aspects such as planning timeframes.