Purpose of the Study
This thesis addresses the growing challenge of urban heatwaves by developing a decision-support system (DSS) called NBScenarios, which helps policymakers implement Nature-Based Solutions (NBS) to mitigate urban heat island effects. This study focuses on Paris as a study case, with the overarching goal to propose different options that optimize the configuration of green roofs and trees along streets (replacing parking spaces) to effectively reduce urban heat islands. These configurations balance differently goals that are challenging to reconcile: maximizing the cooling effect, respecting budgetary constraints, prioritising adaptation benefits to the most vulnerable population, while guaranteeing access for all urban citizens to cool spaces.

Problem Statement
Despite the potential of Nature-Based Solutions for urban climate adaptation, their implementation is blocking at the decision-making stage. Existing decision-support systems (DSS) often lack social considerations, limiting their real-world effectiveness. Many DSS are not adaptable across diverse urban contexts and do not integrate multi-objective optimization, which is essential for balancing conflicting priorities like costs and environmental benefits. Moreover, DSS are often complex, limiting stakeholder engagement. This highlights the need for modular, expandable tools that support informed, inclusive decision-making.

Research Approach
This thesis employs a multi-objective optimization (MOO) approach, utilizing the ε-NSGA-II algorithm and integrating spatiality. The selection and definition of case-relevant nature-based solutions, objectives, and constraints, is structured by the XLRM framework, in particular. A spatial grid (100m x 100m cells) covering Paris allows for detailed urban data to be processed and integrated into the DSS. The DSS generates a range of contrasted Pareto-optimal solutions, balancing trade-offs among objectives and corresponding NBS mapping, providing policymakers with a diverse range of options.

Key Findings
The DSS produced a range of optimal and diverse options for NBS implementation in Paris, with green roofs and on-street parking transformed into street trees. Results showed typical values for cooling effects ranging from 0°C to 7.5°C, initial costs spanning from €500 million to €3.5 billion, and clear trade-offs between maximizing cooling and minimizing costs. Solutions effectively revealed the spatial distribution of cooling impacts and highlighted disparities between areas with vulnerable populations and high cooling potential.

Conclusion
NBScenarios provides a flexible and adaptable framework for policymakers to balance environmental, economic, and social objectives in urban climate adaptation. While the study focuses on Paris, the DSS is designed to be applied to other cities facing similar climate challenges, allowing for scalability and adaptability. The findings highlight the system's ability to inform critical policy decisions related to heatwave mitigation, including cost considerations and social justice factors, facilitating trade-offs between competing priorities.

Recommendations
For future work, the DSS could incorporate uncertainties and include other NBS types or objectives. Continued testing in different urban contexts is also recommended to refine the system and ensure its broader applicability. The creation of an interactive dashboard, based on the DSS current output, could further improve stakeholder engagement and decision-making.

Significance of the Study
This research advances urban climate adaptation by integrating multi-objective spatial optimization into decision-support systems for nature-based solutions. Academically, it addresses social factors and NBS mainstreaming while offering a modular, interdisciplinary DSS adaptable to other cities. Societally, the study enhances policy-making and stakeholder engagement, emphasizing social justice in climate strategies. The DSS enables cities globally to scale NBS effectively, bridging gaps between technical design and actionable, socially responsible urban resilience policies.