In today's digitally connected world, the spread of conspiracy theories on social media poses a significant challenge to societal trust and public discourse. This thesis aims to develop a model for identifying conspiracy theories on Telegram, a platform known for its private nature and the use of channels to disseminate information. The main research question guiding this study is: *How can conspiracy theories be identified on Telegram?* To address this, the research leverages graph theory, machine learning, and topic modeling.

The first step involved modeling the network structure of Telegram channels using graph theory. Channels were represented as nodes, and forwarded messages as directed, weighted edges, allowing for the analysis of network structures and the identification of influential channels. Various centrality measures, including weighted degree centrality, betweenness centrality, and viral message centrality, were computed to assess the influence of channels within the network.

A fine-tuned multilingual BERT (m-BERT) model was used to classify Telegram messages as either conspiracy-related or not. This model was trained on a manually labeled dataset and demonstrated robust performance.

To identify specific conspiracy theories, BERTopic, a topic modeling technique, was applied to the messages classified as conspiracy-related. The resulting topics were then analyzed using the OpenAI API, which linked them to known conspiracy theories. The study found that all identified topics could be connected to existing conspiracy theories, suggesting that the model is effective in detecting these narratives on Telegram.

The research also included the validation of the model using a fictive conspiracy theory, "The Verdant Shadow Conspiracy," created specifically for this purpose. This validation demonstrated the model's ability to detect even simulated conspiracies, though it highlighted the importance of continuous refinement and expansion of the dataset.

The contributions of this thesis are twofold: scientifically, it advances the understanding of how to model and analyze Telegram networks to detect conspiracy theories, and societally, it offers a framework that can be used to monitor and potentially mitigate the spread of harmful misinformation. Future research should focus on expanding the dataset, refining the model, and exploring interdisciplinary approaches to further enhance the detection and understanding of conspiracy theories on Telegram.

This research focuses on designing a digital platform that contributes to reducing food waste in Saudi Arabia. While a digital platform may not be an efficient and viable option to tackle this problem in countries with advanced waste management infrastructure, it is a feasible solution for tackling food waste in Saudi Arabia, given the current lack of infrastructure. It offers a quick and scalable approach that can be rapidly deployed to bridge existing gaps. Unlike traditional infrastructure projects requiring significant time and resources, a digital platform leverages existing technology to create immediate connections between retailers, consumers, and other stakeholders. The retail stage includes restaurants, cafes, and bakeries. The main stakeholders of interest are retail businesses. The main research question that guided this research is: What digital platform design can contribute to reducing food waste in the retail sector in the eastern province of Saudi Arabia in alignment with circular economy principles?...

Ransomware attacks, orchestrated by cybercriminal organizations, pose a global threat in our digital era by exploiting system vulnerabilities and demanding ransoms for seized and encrypted data. Conti, a Russian ransomware group, ceased to exist after a 2022 data leak, offering a unique opportunity to study their modus operandi. The leak includes chat transcripts containing indicators of value, like compensation agreements and digital transaction details. By using the value chain lens, these indicators can be used to determine how ransomware groups create and allocate value within their operations. This understanding is essential for law enforcement aiming to disrupt ransomware activities more effectively, as targeting the most valuable components of their operations can result in significant disruptions to the organization. This value attribution is currently unknown. The question this thesis attempts to answer is: How do ransomware groups allocate value to the activities of the ransomware value chain, and how can this inform law enforcement in developing effective intervention strategies? The methodology involves an exploratory research approach to Conti's public chat transcript data, supported by blockchain analysis. The final deliverable includes a value analysis through the value chain lens and recommendations for the FIOD. These recommendations include insights gained from studying the value creation, compensation, and allocation of ransomware groups, highlighting strategic points along the value chain where disruption would result in the most significant impact. These insights are crucial for enhancing criminal investigations and guiding authorities to disrupt valuable and critical activities within ransomware operations effectively.

As the Netherlands aims to phase out gas by 2050, alternative heating solutions are urgently needed, especially in densely populated areas like Amsterdam's city center. Solutions like hydrogen and green gas are infeasible due to limited availability.
A potential solution is a fifth-generation district heating and cooling (5GDHC) network, an energy system that provides both heating and cooling to buildings using external energy. This is possible due to a low temperature heat carrier in combination with bidirectional operation. Heating and cooling can be exchanged between buildings within the network, reducing the need for external energy. A 5GDHC network for a large city requires clustering of the buildings to create smaller networks, which can later be connected. Clustering allows for a bottom-up approach, reducing both the investment risks and the overload risk. A benefit of 5GDHC is the possibility to use aquifer thermal energy storage (ATES), where thermal energy is stored underground. However, research on 5GDHC, particularly on large-scale implementation, is scarce. Dividing buildings into clusters is crucial for large-scale implementation, since it reduces the investment and overload risk, but the best methods are unknown.
Additionally, the possibility of implementing ATES is not considered in research during the clustering process. This research investigates a method to cluster buildings and integrate ATES systems within these clusters. The goal is to create compact clusters to minimize the use of space in the already crowded subsurface. The clusters must also have a high demand fulfillment, meaning the heating and cooling demands must be fulfilled as much as possible within the cluster, either through energy
exchange between buildings or through the use of storage. A higher demand fulfillment reduces the need for external energy. The developed method clusters buildings based on their geographical location using both k-means and equal size k-means to ensure compactness.After this, ATES systems are implemented in the clusters, which can only be placed in available open space. The model results indicate that implementing ATES systems can increase the demand fulfillment from 1.4% to 58.6% for the entire center of Amsterdam, if buildings are insulated to a level fit for low temperature heating. The results show that very compact areas struggle more to meet heating demand. k-means outperforms equal size k-means in both compactness and demand fulfillment, making it the recommended method. The findings suggest that a 5GDHC network with ATES implementation has significant potential, with the most potential for clusters in the east and northwest. The identified method is robust, adaptable to various geographical regions, and particularly effective in densely populated urban areas with limited space and high demand. In conclusion, this research contributes to the fields of aquifer thermal energy storage and fifth generation district heating and cooling. It not only fills critical gaps in existing literature but also provides a practical methodology for optimizing urban energy systems. This approach holds promise for supporting initiatives like the ‘High-hanging fruit’ project by the AMS Institute, assisting sustainable urban heating solutions in Amsterdam and beyond.

The Affordable Rent Act (Wet Betaalbare Huur), enacted on July 1, 2024, establishes regulations on rent control in the mid-market rental sector. This is accomplished by establishing maximum rents in the mid-market rental sector based on the quality of housing, as determined by the WWS points system. The study critically examines the impact of this legislation on the governance of housing associations in the West-Brabant and Tholen regions through a combination of literature review and semi-structured interviews with key stakeholders.

The findings are threefold: First, the Act has a limited direct impact on housing associations, as their rents typically fall below the new limits. Second, the enforcement of the WWS points system introduces significant administrative challenges for housing associations. Third, the anticipated exit of investors from the mid-market sector may increase demand for social housing, thereby placing additional pressure on housing associations.

This study addresses a gap in the existing literature by focusing on the effects of the Affordable Rent Act on housing associations, as previous research has primarily concentrated on investors. The study concludes with policy recommendations, including the establishment of a central housing agency, the creation of a mid-market rental fund, and a continued focus on social housing by housing associations. The recommendations are designed to assist housing associations in navigating the dynamic housing market.

Responding rapidly to epidemic outbreaks presents significant challenges, due to resource, capacity and time limitations. Anticipatory Action (AA) is a newly emerging strategy in the field of humanitarian aid, designed to preemptively address potential crises. By taking impact-reducing actions before a disaster strikes, AA seeks to minimize human loss. However, AA frameworks currently use static prepared-in-advance plans. As a result, AA is not sufficiently able to deal with the uncertainty levels in the onset and spread of epidemics. Effective epidemic control requires plans that can adapt to a constantly changing environment and incoming information, such as the number and location of suspected cases, weather forecasts and population movement, while balancing flexibility with an effective management approach. We show how the (DAPP) framework for decisionmaking under deep uncertainty can be adapted to enhance the common anticipatory action approach with flexibility and effective management for epidemic control. More specifically, we show how DAPP allows to take into account newly available information and change the strategy to minimize human loss. We illustrate it with a case study of cholera in Cameroon, for which the French, Netherlands, and Cameroon Red Cross, supported by EHESP, are developing an early action protocol and a model that assesses the cost-effectiveness of actions for different risk levels and external shocks. Our results suggest that DAPP increase flexibility and coordination in anticipatory action for epidemics and helps optimizing early action strategies, which could have larger implications for global disease control.

Access to affordable, reliable, and sustainable energy is a key goal of the Sustainable Development Goals (SDG). Despite this, 675 million people globally, especially in rural areas, remain without access to electricity. Sierra Leone exemplifies this issue, with only 4.9% of its rural population having electricity access. The lack of access hinders economic and social development, impacting healthcare, education, and overall quality of life. While grid extension is costly and difficult in remote areas, solar-based micro-grids offer a promising solution for rural electrification, leveraging the country's solar potential.

However, literature reports performance issues with these micro-grids, and while some factors influencing the performance of micro-grids are identified, their impact and mitigation strategies are underexplored in rural contexts. This research aims to identify and classify the factors affecting micro-grid performance and assess their impact on rural developing areas. The study provides insights into mitigation strategies, considering technical, social, economic, and governmental contexts, bridging the gap between qualitative and quantitative research to improve access to electricity.

The research uses a case study approach combined with modelling. Data collection includes site visits, literature reviews, and semi-structured interviews. The modelling uses Python for Power System Analysis (PyPSA) to assess the impact of identified factors on micro-grid performance. The case study focuses on four small communities in Sierra Leone with varying levels of user satisfaction. Findings reveal that micro-grids face economic constraints, technical limitations, and dependency on government support.

The study identifies several key factors affecting micro-grid performance, including high appliance use, low-quality battery design, and a lack of skilled technicians. Battery performance is determined as the most critical factor, directly affecting electricity availability. The research evaluates several mitigation strategies, such as Demand Control (DC), air conditioning, and additional battery capacity. DC is found to be an effective mitigation strategy, especially in evening hours, enhancing electricity access in rural areas.

Demand Control is a promising mitigation strategy and can enhance micro-grid performance, within the rural developing context. Further research is recommended to refine these strategies and explore their broader applicability to other micro-grid systems in rural developing areas.

Indonesia started its journey towards a net-zero energy system. Research and developments towards these targets are currently well underway. However, present-day prediction models show that the rising demand of the island will not be met with the renewable energy technology potential. One solution for this problem is the development of power interconnections between the power systems of the Archipelago. However, short-term planning and speculations about the feasibility of inter-island interconnections block their construction. This study aims to explore the feasible options for inter-island interconnections and recommends the next steps that can be researched. The method to answer this research question is through the combination of a techno-economical model and an institutional analysis. The techno-economical model utilizes the energy planning model ’Calliope’, as the foundation of this model was already developed by researchers at TU Delft. To include geographical features, a GIS (Geographic Information System analysis) is performed and its outcomes are used to analyse the results coming from the Calliope model. The results from this techno-economical analysis are then used as input for the institutional analysis. The latter is built upon the existing Williamson framework. Due to the limited scope and timeline of this study, the aspects planning of transmission infrastructure, financing of transmission infrastructure and contractual arrangements between public and private parties are chosen as focus points in the institutional analysis. The state-of-the-art technology used for inter-island interconnections, or submarine power cables in general, are High-Voltage Direct Current (HVDC) cables. The advantage of HVDC over HVAC (High-Voltage Alternating Current) cables is their ability to transmit power over longer distances while creating less envi- ronmental impact due to limited electromagnetic interference. The planning of submarine power cables is a relatively new subject in academic literature. The impor- tant geographical features to consider in the design process of submarine power cable routing found in this study are the depth of water (bathymetry), seabed slope, seismic regions, and protected zones. These ge- ographical features can be categorized in terms of their impact on the cost and risk of breakage of the cables. This study found through a techno-economical analysis that there are multiple inter-island intercon- nections that are found to be needed in a Calliope power model of Indonesia and feasible considering the aforementioned geographical features. The visual shortest-path routing of these feasible cables has been determined through an AHP (Analytic Hierarchy Process), analysis combined with a Dijkstra shortest-path algorithm. On the institutional side of the result, this study found that there are multiple barriers that could form a barrier to the development of inter-island interconnections. The main issues found have to do with the planning, financing and contraction of the institutional side of transmission infrastructure development. In order to soothe these barriers the following recommendations are constructed; using an integrated transmission capacity planning approach, institutional strengthening for investment and creating a governance body dedicated to interconnectivity. The conclusion of this study is that there are feasible interconnections for accommodating renewable energy technology integration in Indonesia. The found feasible routes of this methodology consist of Bali- Nusa Tenggara Barat, Kalimantan Barat-Daerah Khusus, Jakarta-Kalimantan Tengah, Jawa Tengah-Kalimantan Selatan, Jawa Timur-Kalimantan Tengah, Jawa Barat-Lampung Banten, and Kepulauan Riau-Riau and between Kalimantan Barat and Jawa Barat. Institutional adjustments are found to be crucial for fostering this interconnected system. Institutional recommendations involve integrating interconnections into the RUPTL (Electricity Supply Business Plan), strengthening foreign investment with clear policies, and ensuring transparent contractual arrangements between public and private entities. Moreover, prioritizing grid balancing and exploring sustainable financing methods like green bonds further support these efforts. The most important limitations of this study are the minimal use of scenarios, the use of only one energy system modelling software (Calliope) and the limited scoping for the institutional analysis. As for future research, this study invites researchers to think about the specific power flow of the system, the operational challenges, social engagement and public perception and comparison to other archipelago countries

Critical infrastructures, such as the energy sector, are vital for the proper functioning of society. It is crucial to protect these infrastructures against cyberattacks on their operational technology, ensuring their operational performance, and safeguarding energy safety. There is a lack of existing research on improving cybersecurity in operational technology for increasing operational performance in liquefied natural gas infrastructure in the Netherlands, specifically when it comes to increasing redundancy in the system. In an attempt to fill this knowledge gap, this thesis implements agent-based modelling to answer the following main research question:

“To what extent does the implementation of redundancy enhance the operational performance of operational technology in the industrial processes of Gate terminal in the face of cyber threats, in order to maintain the availability of business services?”

The thesis shows that the key determinants for the effectiveness of a redundancy strategy are the criticality of the redundantly implemented element, and the degree of diversity applied. Additionally, a trade-off between the advantages of redundancy the inevitably increased attack surface can be observed: when implementing an ineffective redundancy strategy, overall system performance may degrade in comparison to the initial system conditions. The importance of an effective incident response cycle must not be overlooked. A redundancy strategy is only as effective as the capability of the system operator to prevent and deal with incidents.
Future research can build on this thesis, exploring more explicit redundancy strategies, types of attacks, and strategic defense systems, as well as the optimization of the redundancy trade-off. These research directions can help in further analyzing and understanding the impact of redundancy on system operational performance.

High-speed rail (HSR) is gaining increasing attention due to its sustainability and transport capacity, aligning with ambitious European transport goals for 2030 and 2050. Despite its potential, the creation of a unified European HSR network faces challenges rooted in poor coordination and national interests. This study addresses the absence of a comprehensive, long-term strategy for establishing such a network, with a specific focus on critical infrastructure development. It introduces an iterative network growth model to determine where, when, and at what cost HSR infrastructure should be built under centralized decision-making processes given current budget allocations. The approach analyses the dynamic interaction between infrastructure expansion and the long-distance transport market demand distribution. Results emphasize the benefits of adopting centralized decision-making and appraisal processes, highlighting that achieving these goals requires a comprehensive, collaborative effort, as well as a proper European institutional investment management with more spending power.

This research focuses on public attitudes toward nuclear energy in the context of reaching consensus on energy solutions for climate action. Using the Social Identity Approach, it analyzes how social groups shape opinions, with a spotlight on Germany and France. Political affiliation, age, socio-economic status, and gender are key identity factors. The study employs an agent-based model, revealing that discussing nuclear energy within multiple identity contexts promotes consensus and depolarization. The findings underscore the significance of social identities and context in shaping opinions, transcending the nuclear energy discourse to advocate for inclusive discussions in decision-making processes. Future research can delve into social influence, ongoing debates, and additional factors to comprehensively understand nuclear energy attitudes.

When it comes to the modification of a fossil- to bio-refinery, the reuse of a standardized design is unprecedented. Factor in the presence of stakeholders with varying interests and opinions equates to a highly complex system. Alignment of the stakeholders is therefore recognized to be necessary. The possibilities on how to make such a refinery modification less complex through the alignment of the relevant stakeholders became the goal of this study. To achieve it, a framework has been developed, and the necessary data to construct it shall come from interviews with experts from the field and literature reviews. The findings show that alignment of the stakeholders on the requirements that need to be considered for
modification projects can help in increasing the success of the implementation of a
standardized refinery modification design. To test the framework, it was implemented in an engineering company’s refinery modification project. Out of the framework implementation advice has been formulated on how to further help the engineering company to become successful in aligning their stakeholders and to effectively implement a standardized refinery modification design.

The Dutch government has set a goal to phase out the use of natural gas by 2050. As a result, municipalities are focusing on implementing alternative heating strategies in certain neighbourhoods. However, districts with older building stocks are often neglected, raising concerns that city centers with older, poorly insulated buildings and high heat demands may be left until later stages of the transition, despite the need for a clear heating strategy in these areas. Additionally, urban areas face the challenge of the urban heat island effect, which leads to trapped heat and elevated summer temperatures. This effect, coupled with the consequences of climate change, will increase the demand for cooling.
To initiate a new heat transition, it is crucial to adopt new sustainable and locally generated heat systems. A potential solution is the implementation of a 5th District Generation Heating and Cooling (5GDHC) network, which utilizes low-temperature heat and cold, potentially sourced from waste heat. However, the design and implementation of a 5GDHC network presents numerous challenges, including the absence of comprehensive guidelines and limited knowledge regarding the deployment of these systems on a larger scale or in an area with an older building stock. This research focuses on developing a tool that can identify potential clusters for a 5GDHC system in densely populated urban areas.
To achieve the research objective, a methodology is developed to identify clusters for a 5GDHC network. Clusters are essential for the implementation of a 5GDHC system in a larger area, as they mitigate investment risks and facilitate a clearer implementation process. The methodology in this study integrates the Single Linkage clustering algorithm and Geometric Graph Theory, which are extended into a model. This model generates clusters based on building locations and energy profiles, and assesses their performance using metrics such as the aggregated hourly lack of supply throughout the year and the total length of the pipe network.
A case study of the inner city of Amsterdam, part of the 'high hanging fruit' project by the AMS Institute, is utilized to test the model. The model requires data on potential waste heat and retrofitted buildings as input. The developed model effectively identifies clusters within a large urban area based on building locations and energy profiles. Trade-offs between pipe network length and energy efficiency must be considered when evaluating the model's results. It is highly recommended to adopt a bottom-up approach and establish 5GDHC clusters incrementally within the city. The hourly disbalances, calculated by the model, can identify potential clusters ready for connection. Moreover, the performance metrics derived from the model can serve as valuable decision-making guides during the design phase of 5GDHC networks. To enhance the decision-making process further, it is crucial to integrate the model's information with urban planning considerations and engage relevant stakeholders. By combining these factors, a comprehensive and well-informed decision-making process can be facilitated, leading to more effective and efficient 5GDHC network designs and implementations.
The full model created within this research can be retrieved from: \url{https://github.com/svanburk/clustering5GDHC.git}

Forest BORG, a novel framework using the BORG multi-objective evolutionary algorithm, optimizes policy trees for socio-economic systems. Tested on real-world case studies, Folsom lake and RICE IAM, it addresses multi-objective challenges under deep uncertainty, offering effective and explainable decision support for climate change policies.

The 'Forest BORG' framework, based on the BORG multi-objective evolutionary algorithm (MOEA), is introduced as a method for optimizing policy trees in socio-economic systems. This framework accommodates tree-structured decision variables through specialized evolutionary operators, enabling multi-objective and explainable decision support. Tested on the Folsom lake model and the Regional Integrated model of Climate and the Economy (RICE) Integrated Assessment Model (IAM), Forest BORG addresses the challenges of optimizing multi-objective problems under deep uncertainty, primarily driven by climate change.

The Folsom lake model and RICE IAM serve as suitable case studies due to their reflection of real-life problems affected by climate change and involving multiple stakeholders. The XLRM framework is employed to operationalize the conceptual models of these case studies, categorizing uncertainties (X), levers (L), relations (R), and metrics (M).

Positioned between current MOEA capabilities and the need for explainable policies in real-world multi-objective problems under deep uncertainty, Forest BORG undergoes rigorous testing. Analyses include run-time dynamics, Forest BORG specific evolutionary operator dynamics, controllability, exposed trade-offs, and highlighted policy trees.

The framework employs seven mutation operators, crossover with prune and bloat control operations as the recombination operator, and an adaptive tournament size function as the selection operator. The performance and behavior of Forest BORG are evaluated through the five analyses mentioned.

Run-time dynamics demonstrate the algorithm's effective convergence based on hypervolume. Search operator dynamics investigate novel mutation operators, showcasing their contribution. Controllability analysis explores the impact of hyperparameter settings on algorithm performance. Exposed trade-offs are visualized through pareto-optimal policy alternatives, emphasizing the selection of compromise policies. The highlighted policy trees provide insight into actionable decisions based on different objectives.

Forest BORG proves effective, efficient, and reliable across case studies. Comparative analysis with the original policy optimization tree (POT) algorithm reinforces its effectiveness. Although the expected shift from subtree to point mutations was not observed in all cases, the search operators enhance overall search capability. Hyperparameters are case study-dependent, with modest maximum depths favored to avoid overfitting.

Forest BORG successfully produces pareto fronts for each case study, revealing inherent trade-offs. The selected policy trees align with performance metrics, providing valuable insights for decision-makers. However, some results exhibit counter-intuitive behavior, notably in the RICE model trade-offs and small peaks in run-time analysis, attributed to epsilon values.

The research suggests further exploration of other IAMs for a more realistic representation of reality. Forest BORG's societal contribution lies in its development and application to real-world case studies, expanding the range of multi-objective decision-making tools for socio-environmental systems. The implications extend to addressing climate change through policy development, emphasizing the potential for broader applications beyond the tested case studies.

The concept of deterrence, using fear of punishment to encourage compliant behavior, is widely discussed. However, deterrence often places an emphasis on the economic side of compliance while neglecting other possibly crucial factors, as is argued by the literature. Psychological factors, notably the personal norm and the social norm, often appear to play important roles in the decision to comply or not. The personal norm describes an individual’s attitude and moral stance toward specific behaviors, such as compliance. On the other hand, the social norm revolves around perceptions of others’
behavior and opinions within one’s social network. Hence, the influence of these factors is researched within different populations, and different environments, some enabling and some impeding compliance. A new framework to encompass all these elements is explored with Agent-Based Modeling and applied to a case study in collaboration with the ILT (Inspectie Leefomgeving en Transport). Findings included the lack of a social influence on compliance, whenever the personal norm was high and the necessity to be able to comply in order for deterrence to show an effect. For future research, suggestions are done to improve the framework and to explore additional aspects.

Governmental organisations worldwide are working on their open data strategies and are openly sharing their data with the public. Recently also open data communities have been set up by several national governments. The effects of setting up these data communities on open data benefits and barriers and whether an effect exists at all is not yet clear, because there has been little scientific research into data communities. This thesis therefore focuses on the effects of setting up open data communities. 
A single-unit case study was conducted to gather qualitive information of the effects of data communities. The case study focussed on the Dutch open data community and consisted of two parts, a document analysis and in-depth interviews. The results of the analysis show how the Dutch community contributes to enhancing open data benefits, including (indirectly) creating more informed citizens, increasing the access to capacity and resources outside of the data publishing organisation and a higher problem-solving capacity. Furthermore, the interview participants agree to a large extent that communities contribute to intragovernmental collaboration and the use of collective intelligence to solve public problems. According to the interviewees, the community also (potentially) mitigates open data barriers such as the lack of interest in using open data (by governmental organisations). The interviewees stated that the community managers made sure every question that was posted got a sufficient answer within a reasonable amount of time and therefore the barrier stating that the data provider ignores requests and suggestions of data users could also be mitigated, as well as difficulties in the interaction with the data provider. Both researchers and most community users and managers argued that the community could also contribute to mitigating low engagement of public managers with open data and increasing the knowledge and skills of employees to use the open data. Lastly, according to the interview participants, the community can decrease difficulty in discovering/locating data and not being able to combine and connect datasets. The interviewees were also questioned about how institutional instruments could increase the value of the open data community. Although the participants concluded that the contribution of formal instruments (such as rules) is limited, they indicated informal rules (such as norms) and enforcing instruments (such as rewards) can contribute to the value that is created by an open data community. 
The community-specific challenges identified in the analysis can contribute to the process of designing an open data community, because policy makers can compare scope and design choices with the empirical experiences. This thesis also provides an exploratory scientific contribution: this research provides insight into how open data communities can contribute to enhancing open data benefits and the mitigation of open data barriers. Although the results are promising, certain limitations are applicable to the research. The thesis only studied one open data community and the full list of open data benefits and barriers was reduced based upon an assessment of the author of the thesis. Last of all, the interviews focused on qualitative data. Combining the results of this thesis with other case studies can improve generalisability.

This study aims to investigate and develop the efficiency of a decentralised dynamic system for last-mile parcel distribution. A better understanding of the efficiency, and thus the feasibility of this decentralised logistics method, contributes to a better understanding of the potential of these systems as alternatives to the current centrally organised system. The main research question is: To what extent can the efficiency of a decentralised dynamic parcel distribution method be improved by applying different heuristics and adapting the simulation environment with congestion and a mixed fleet? To answer this question, a review is made of existing literature on last-mile logistics, self-organisation in logistics and auction methods in logistics. The current model is evaluated, benchmarked and validated. Model improvements are defined and applied in an experimental setting using a small dataset, after which the extended model is applied to a real-scale case dataset based on 729 delivery addresses in Delft. The applied modification strategies are random insertion, parcel swapping and k-means clustering. To account for the behaviour of the model under different conditions, the method was tested for a heterogeneous fleet of vans and cargo bikes and different levels of congestion. In general, the heuristics were able to improve the performance of the model, but the improvements in travel distance were minimal. When the combination of the best performing heuristics is applied to the case data, a 4.6% improvement in vehicle travel distance can be achieved compared to the original method, but still with high computation times. Comparison with an external centrally organised OR solver shows that although the decentralised method can be improved, it does not perform well, as the OR solver can find a better solution in a fraction of the time needed for the improved decentralised auction method. This study provides a proof of concept of the method and a more efficient model has been constructed. However, given the limitations of the method, further research should carefully consider whether it makes sense to continue with this particular method.

Enhanced textile reuse is required due to the textile industry's increasing environmental pressure. In order to reach this, behavioural change is necessary. However, it is currently unknown which changes in consumer values that determine textile reuse consumption are
most effective. In addition, rebound effects appear: reuse lowers the impact of textile consumption, but also causes higher levels of consumption, reducing its benefit. This research provides in-depth understanding of textile reuse rebound effects and model-based advice on the effectiveness of consumer value changes that reduce environmental impact, while accounting for consumer heterogeneity.

The growing global population combined with improved prosperity has increased the energy demand. The consequence of this growing energy demand is an increase in greenhouse gas emissions leading to climate change. The irreversible and worrying consequences of climate change have led to various national and international targets. In the Netherlands, national targets are integrated into policy making in 2019, distinguishing between five sectors. This study aims to describe a systematic approach that contributes to facilitating decision-making in the heat transition in the built environment. The approach consists of three steps, considering the system elements of a socio-technical environment. The first step of the systematic approach is formed by an actor analysis, where through an actor identification and categorization the non-rational aspects are mapped. These non-rational aspects, consisting for example of willingness to participate in the heat transition and underlying technological preferences, can be used to determine the social feasibility of a heat transition vision. The second step in the systematic approach is the application of the techno-economic Vesta MAIS model. This model can calculate at a neighborhood level per heat transition vision the national cost and share of energy carrier. In this way, the model contributes to facilitating decision-making at the municipal level. To enrich the technical aspects of the Vesta MAIS model with insights into grid congestion, this study proposes a power flow analysis as the third step in the systematic approach. This power flow analysis enables municipal decision-makers to analyze the impact of the heat transition on the existing power grid at an early stage.