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Beyond Good Intentions: A Critical Analysis of Maladaptation Risks in Climate Change Adaptation Household Measures 52.25

Assignment number: 52.25

Start of the project: ASAP

Required course(s): Urban Resilience in a Changing Climate

Involved organisations: Various municipalities

Climate change adaptation is essential for reducing vulnerability and enhancing resilience. On the municipal scale, 50-70% of the surface area is privately owned. However, 60% of municipalities have subsidies to reduce water nuisance. How can these funds effectively be used towards measures on privately owned land? Already municipalities invest in measures such as garden tile removal, subsidies for rain barrels or green roofs, and green coaches. However, not all adaptation is beneficial. Some actions inadvertently increase vulnerability or reduce future adaptive capacity, or have negative impacts on other sectors.

This phenomenon is known as maladaptation. It is increasingly recognized as a critical challenge in climate governance. As defined by the Resilience self-assessment tool, ‘maladaptation refers to the process that an intentional adaptation action may lead to negative effects which increase vulnerability, diminish wellbeing or undermine sustainable development. This can happen in the same or other regions, systems, sectors, or social groups than those targeted by the adaptation action’ (Regilience). While this phenomenon has some examination at a national level, there is still a research gap on the local level.

This project aims to examine the risk of maladaptation for commonly used municipal policy instruments that aim to stimulate adaptation by Dutch households, and how this risk can be reduced. You will build upon previous research (Evers 2025) with specific attention to maladaptation based on Magnan (2014, 2016) and Higuera Roa (2025).

For this research, you will closely collaborate with WDO Delta and municipality Hardenberg. Through these organizations you will also work with various municipalities (Hengelo, Hellendoorn, Enschede, etc.), waterboard, province. Data will be collected using a combination of qualitative methods (documents, assessment tools, interviews).

Further information

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Supervision

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