This thesis investigates the comparative performance of three Multi-Objective Evolutionary Algorithms, Epsilon-NSGA-II, Borg, and Generational Borg, within the Evolutionary Multi-Objective Direct Policy Search framework, applied to the optimisation of the JUSTICE Integrated Assessment Model. By combining benchmark problems (DTLZ2 and DTLZ3) with the high-dimensional, multi-modal JUSTICE model, the study evaluates convergence dynamics, computational efficiency, and solution quality across varying levels of parallelisation. Results show that Borg consistently outperforms the other algorithms, particularly under high problem complexity, due to its asynchronous, steady-state architecture and adaptive operator features. Generational Borg performs better than Epsilon-NSGA-II on complex problems, but both suffer from scalability limitations due to synchronous execution. These findings underscore the importance of algorithm selection in climate-economy policy modelling, offering guidance on how MOEA design and computational resources interact to affect optimisation outcomes.

This thesis explores the potential of increased recycling rates as a strategy to mitigate emissions, reduce waste accumulation, and alleviate material depletion across diverse regions and socioeconomic contexts. By developing and applying the JUSTICE-MATTER model, this research advances our understanding of how recycling, as a key component of a circular economy, can be integrated into broader climate policy frameworks to achieve sustainability goals.

The research methodology involved the development of a comprehensive matter use module based on the economy-wide material flow analysis (ew-MFA) framework that tracks material flows from extraction to disposal. This MATTER module provides a granular understanding of material usage across 57 regions, considering regional disparities in resources, economic activities, and socio-economic conditions. The JUSTICE-MATTER model integrates these material flows with climate and economic modules, offering a nuanced evaluation of how recycling policies impact emissions reductions, economic output, and material depletion under different Shared Socioeconomic Pathways (SSPs).

The findings highlight that recycling plays a significant role in reducing waste and conserving material resources, especially in regions with underdeveloped waste management systems. The transition to a Circular Economy (CE) in regions like Southeast Asia, Brazil, and parts of Africa resulted in substantial reductions in waste generation and material depletion. However, the impact of recycling on emissions reduction was found to be relatively modest, particularly in high-emission regions like China and Brazil. This suggests that while recycling is essential, it must be complemented by other strategies, such as transitioning to cleaner energy sources and implementing comprehensive emissions control policies, to achieve meaningful emissions reductions.

Economically, the effects of increased recycling rates varied significantly across regions. Regions with established recycling infrastructure, particularly in Europe, experienced minimal economic disruption from higher recycling rates. In contrast, regions like Chile and Brazil faced higher economic costs due to the need for substantial investment in upgrading recycling systems. These findings underscore the importance of tailoring recycling strategies to regional contexts, considering the economic and infrastructural disparities that influence the feasibility and impact of such initiatives.

The research highlights the importance of region-specific policies, emphasising differentiated approaches that address the unique challenges and opportunities of each region. While developed regions may focus on optimizing recycling processes, developing regions must prioritize infrastructure and capacity-building efforts. Despite the advancements in the JUSTICE-MATTER model, limitations such as static emission factors and a narrow focus on certain materials suggest areas for future refinement. Future research should incorporate dynamic emission factors, expand material analysis, and regionalise key parameters to reflect diverse economic and material use patterns more accurately.

In conclusion, this thesis contributes to advancing Integrated Assessment Models by integrating material flow dynamics into the JUSTICE-MATTER framework. Addressing the identified limitations and pursuing suggested future research directions will enhance the model’s capacity to guide global sustainability efforts and offer more robust policy recommendations for the complex challenges of the future.

Recent advancements in artificial intelligence (AI), particularly in deep learning, have significantly enhanced AI capabilities but have also led to more complex and less interpretable algorithms. This research addresses the challenge of Explainable AI (XAI) by focusing on enhancing the interpretability of AI decisions through the use of Explainable User Interfaces (XUI). The study identifies two primary knowledge gaps: the predominance of XAI research targeting technically skilled users, neglecting the end-user who often lacks technical expertise, and the insufficient exploration of user-centric design principles in real-world XUI applications.

The research adopts the Design Science Research Method (DSRM) to develop an XUI tailored for the FOKUS project, which uses Electrocardiogram (ECG) data to detect myocardial infarctions. The study emphasises the strategic application of interactive design principles such as complementary naturalness, flexibility in explanation methods, and responsiveness through progressive disclosure to improve the system’s interpretability. Notably, sensitivity to context and mind, though not initially implemented, emerged as a critical design principle from the analysis and was subsequently positioned at the pinnacle of a restructured pyramid model of design principles.

Key findings highlight the effectiveness of the selected design principles in enhancing interpretability and underscore the importance of involving stakeholders early in the development process to align the XAI and XUI with end-user needs. The research proposes a structured design approach framework for XUI, involving sequential phases from pre-XAI to XUI design, to systematically integrate user feedback and improve the design iteratively. The proposed framework restructured pyramid model of the design principles aim to guide future developments in XAI and XUI, enhancing their practical application and effectiveness in various contexts.

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.

Novelty search is a state-of-the-art approach focusing on behavioural novelty, rewarding diverging, as opposed to pursuing static objectives. This is relevant for exploratory modelling and analysis, which focuses on exploration of model through open exploration or directed search. Novelty search as an open exploration strategy is being tested against proven methods such as latin hypercube sampling. Using existing evolutionary algorithms and a developed novelty function, the experiments focus on comparisson, impact of the number of functional evaluations and the impact of the goals of the evolutionary algorithm. Finally it can be concluded that novelty search finds novelties in the lake problem, which makes it a relevant search strategy, but not suited for indiviual exploration. That means that it would still be advised to use latin hypercube sampling for earlier exploration.