This study presents a comparative analysis of the three train-bus timetable synchronisation approaches in order to improve the performance and attractiveness of integrated public transport networks in rural areas. While the existing literature mainly focuses on synchronisation approaches separately, limited attention has been given to comparing the different synchronisation approaches, with the incorporation of passenger demand. To address this gap, this research develops a Mixed-Integer Linear Programming (MILP) model to compare the three synchronisation approaches: train-first, bus-first, and simultaneous synchronisation. Applied to a rural public transport network in Friesland, the model minimises total passenger travel time weighted by passenger demand. The case study, consisting of one train line and thirteen connecting bus services, provides a manageable evaluation of the model. The results show that simultaneous synchronisation consistently outperforms the sequential approaches, achieving the lowest total passenger waiting time for most operating hours and service lines. Comparing the simultaneous synchronisation approach to the current timetable shows significant improvements in total passenger waiting time across all operating hours and for most of the lines. A mode choice analysis using a Multinomial Logit (MNL) model shows an increase in ridership based on the improved timetable, which facilitates a modal shift from private car to public transport. These findings show that timetable synchronisation alone, without improving infrastructure or the addition of more services, can increase the attractiveness of the public transport network. These findings are especially important in rural areas where car dependency is typically higher. The results suggest that transport authorities should reconsider the current sequential timetable synchronisation practices. Optimisation of multimodal networks simultaneously can improve the performance and attractiveness of integrated public transport networks in rural areas.

This thesis investigates how nature-based pilot projects in the Eems-Dollard estuary emerged, evolved, and what challenges they face in scaling up. The estuary struggles with high sediment loads, ecological decline, and climate pressures. To address these issues, pilots such as the Brede Groene Dijk, Dubbele Dijk, Kleirijperij, and Rijpdijk were launched. They test whether sediment can be reused for dike reinforcement and habitat restoration, combining flood safety with ecological and regional benefits. A qualitative case study approach was used, combining literature review, document analysis, field visits, and semi-structured interviews with stakeholders. The analysis applied the concept of the “pilot paradox,” which highlights the tension between the conditions that make pilots successful in isolation and those needed for long-term adoption. The findings show that pilots benefit from strong leadership, temporary governance flexibility, and technical creativity. They demonstrate innovative solutions, such as using ripened clay for green dikes or creating multifunctional spaces between dikes. However, they face major barriers: rigid regulations, fragmented land ownership, complex logistics, and short-term funding. Adaptive steps, like the creation of the Rijpdijk sub-pilot, were essential to keep projects moving forward. Overall, the study concludes that pilots are valuable for testing innovations and mobilising coalitions, but they do not automatically scale into permanent policy. Successful upscaling requires deliberate integration into legal frameworks, funding schemes, and long-term governance. The Eems-Dollard case highlights both the promise and fragility of pilots and provides broader lessons for embedding nature-based solutions into climate adaptation strategies.

Urban environments are getting warmer and warmer because of climate change so there is a need for interventions that can help with cooling. These interventions should also provide co-benefits. A lot of microclimate studies only focus on a few fixed points, or street-wide averages and rarely look at conditions adjacent to an intervention or along a route regarding thermal comfort. Co-benefits such as added bioretention surface area and CO₂ uptake are almost never looked at alongside thermal comfort. To tackle those gaps three nature-based solutions (NBS) scenarios are tested in a thermally uncomfortable street in Amsterdam on the hottest day ever recorded in the city (25 July, 2019), these NBS scenarios are: green façades, bioswales and vegetated pergola. The ENVI-met model had input data of a local weather station and was validated against observations of the Schiphol weather station. So it is used to look at the whole-street conditions and 2 meter wide buffer zones for each NBS intervention at 18:00 and 23:00. A walk-based Physiological Equivalent Temperature (PET) tool of ENVI-met showed proper pedestrian exposure during a route. The Timorpleinbuurt-Zuid neighborhood was selected as a study-case because it is a neighborhood with a lot of high heat exposure and it has a low amount of public green space, so a high impervious cover, but also has urgent rainwater risk, and a high social vulnerability to heat, and it provides local weather data for validation. The results depend on both NBS type and analysis scale (bufferzone or whole street). The pergola has the strongest relief at 18:00, largely seen within its buffer zones where shade covers the sidewalk. Green façades cool more consistently at the street scale and are also staying effective into the evening, while also improving comfort in the bufferzones. The bioswales provide a low amount of thermal comfort at both scales but it has the highest amount of added bioretention surface area and the highest reduction in CO₂ levels. So there are trade-offs so a mixed strategy is recommended. This would be having pergolas on the sun exposed walking routes, green façades to block incoming sunlight on building facades to help with whole street-scale and thermal comfort at 18:00 and 23:00, and bioswales to increase the bioretention surface area and air-quality goals. The bufferzone and walk-based PET are a good way to evaluate the single NBS interventions where people actually move.

Generative AI is rapidly becoming a key part of knowledge work, offering opportunities to increase productivity, creativity, job satisfaction, and improve learning outcomes, while also raising concerns about potential skill erosion as tasks are increasingly delegated to GenAI systems. Despite growing research on productivity, ethics, and governance, limited attention has been given to how knowledge workers manage their interaction with GenAI to sustain professional skills, which are critical for personal development, organizational efficacy, and societal resilience.

This thesis aims to enhance understanding of how GenAI impacts professional skills and whether knowledge workers self-regulate their use to counteract skill changes. Using a qualitative research approach, 38 semi-structured interviews were conducted with knowledge workers across ten sectors. Thematic analysis revealed that GenAI use can result in four distinct skill outcomes: development, maintenance, erosion, and revaluation. Crucially, these outcomes depend on the way workers engage with GenAI rather than the technology alone, with skill revaluation showing that erosion is not always perceived as harmful.

Knowledge workers employ self-regulatory strategies before, during, and after GenAI use, including setting boundaries, tailoring usage to specific situations, critically reviewing outputs, and rewriting or documenting results. Engagement in self-regulation is shaped by drivers across interactional, organizational, and societal contexts, yet organizational support for mitigating risks of skill erosion remains limited, leaving responsibility with individual workers.

By integrating insights on skill outcomes, strategies, and drivers, this study highlights how self-regulation unfolds within a complex sociotechnical system. The findings provide implications for knowledge workers, organizations, and policymakers to design interventions that foster responsible adoption of GenAI, protect professional competencies, and strengthen societal resilience. Future research should further explore organizational-level interventions, governance structures, and profession-specific experiences to ensure skill preservation in the age of GenAI.

Transport infrastructure investments represent some of the largest public expenses, yet systematic ex-post evaluation of demand forecasts remains uncommon, perpetuating systematic biases and limiting institutional learning. This study develops a systematic ex-post evaluation framework using smart card data to investigate multimodal transport demand forecast accuracy, focusing on the Hoekse Lijn metro conversion in Rotterdam. Using OV-chipkaart data from 2020-2025, we compare 2015 forecasts with observed ridership through a four-step methodology: demand reconstruction, systematic comparison, diagnostic analysis via cluster-based station typologies, and quantitative attribution analysis. After correcting for COVID-19 impacts (-12%), results reveal substantial systematic deviations: eight of ten stations underperformed forecasts by 9-40%, with westbound overestimation exceeding 50% at most locations. Despite lower station-level boardings, passenger kilometres exceeded forecasts by 18\% due to longer trip lengths, while beach-related travel generated up to 27 times normal weekday boardings on peak days. Attribution analysis identifies that 60% of residual deviations stem from network representation errors (30%), outdated parameters (20%), socioeconomic forecast errors (5%), and unmodelled e-bike competition (10%), revealing four systematic biases: spatial asymmetry, directional bias, trip purpose misalignment, and underestimated trip length distributions. The study proposes four improvement strategies: systematic ex-post validation using automated data sources, adaptive parameter calibration, scenario-based uncertainty management, and integrated network planning. This requires shifting from "predict-and-forget" to "predict-and-learn" approaches, transforming forecasting into continuous cycles of planning, evaluation and improvement for more accountable, evidence-based transport planning practices.

The increasing frequency and severity of disruptions in global supply chains have underscored the need for resilient port operations. Ports represent critical nodes in the maritime–hinterland interface. Systemic risks arising from disruptive events like droughts, labor strikes, or demand fluctuations can propagate rapidly across interconnected transport networks. Existing research on port resilience largely focuses on isolated components of port operations or infrastructure, offering limited insights into systemic risks that span the entire system. As a result, the interplay between strategies, the adaptive role of stakeholders, and their systemic impacts on the whole supply chain remain insufficiently understood. This study addresses this gap by asking: How can systemic risks in port operations be minimized through resilience-based strategies, identified via a simulation of port disruptions and recovery?

To answer this question, a hybrid simulation approach is adopted, combining discrete event simulation (DES) and agent-based modeling (ABM) in the AnyLogic platform. DES captures port and terminal operations, including queuing dynamics, capacity constraints and resource allocation. ABM represents the adaptive decision-making of inland transport actors such as barge, truck, and rail operators. This integration allows for the simultaneous assessment of process-level efficiency and actor-level adaptability. The Port of Rotterdam serves as the case study, given its status as Europe’s largest multimodal hub and its relevance for resilience planning at the sea–port–land interface. Three disruption scenarios, namely a labor strike, drought affecting inland barge transport, and sea-side vessel demand fluctuations were modeled. Along with these distinct systemic risks, five resilience strategies were evaluated. These include autonomous facilities, dynamic rerouting, inland transshipment hub activation, capacity buffer expansion, and a collaborative strategy combining automation with rerouting. Performance was assessed using container throughput, delay costs, transport costs, delivery rate distributions, facility utilization and recovery time.

The results demonstrate that no single strategy is universally effective across all disruption types. Autonomous facilities enhanced throughput and reduced delays under capacity-limited disruptions such as labor strikes, but offered diminishing returns in the demand fluctuation scenario where a single transport mode becomes loaded too heavily. Dynamic rerouting and hub activation proved most effective minimizing delays in external disruptions such as drought and demand fluctuation, by enabling flows to bypass bottlenecks and stabilizing recovery times. Buffer expansion provided cost-effective shock absorption for short, localized events but lacked adaptability in more persistent disruptions. The collaborative strategy consistently outperformed individual measures, producing the lowest peak delays, fastest recovery times, and stable delivery distributions. However, it also pushed the system closer to full utilization, implying increased operational intensity and potential vulnerability under compounded shocks. These findings align with resilience literature distinguishing between absorptive capacity (buffers) and adaptive capacity (rerouting), while extending the discussion by showing that joint application of measures can generate synergistic resilience effects.

From a practical perspective, the analysis suggests that port authorities should prioritize automation to enhance internal robustness, while logistics providers and carriers benefit more from flexible routing options. The collaborative strategy demonstrates the value of coordinated investment across stakeholders, yet also highlights the importance of governance mechanisms to manage the risks of high-intensity operations. Cost reflections indicate that while automation requires large upfront investments, rerouting-heavy strategies may impose higher variable transport costs, making trade-offs between capital expenditure and operational flexibility central to decision-making.

This study contributes to resilience research by conducting systemic risk analysis through a hybrid ABM–DES model and by integrating both absorptive and adaptive strategies within a comparative framework. While results are calibrated to the Port of Rotterdam, the methodological approach and conceptual insights are generalizable to other large multimodal ports, albeit with performance outcomes contingent on local infrastructural and regulatory conditions. Limitations include simplified operational processes, underrepresentation of customs and scheduling constraints, and the absence of a full cost–benefit model. Future research should extend the simulation with predictive routing algorithms, incorporate detailed economic evaluation, and integrate stakeholder-driven scenario weighting.

Overall, the findings show that resilience in port–hinterland systems is multi-faceted: infrastructure-oriented measures provide robustness, routing measures deliver adaptability, and buffering offers low-cost stability. The combination of strategies, particularly automation and rerouting, can yield superior resilience outcomes but at the cost of higher operational intensity. These insights provide both theoretical advancement and practical guidance for designing resilience strategies that balance performance, cost-effectiveness, and systemic robustness in complex port networks.

This research investigates how employees of small-to-medium-sized enterprises (SMEs) in urban redevelopment areas make transport mode choices for business-related trips, using the KeileCollectief pilot in Rotterdam as a case study. Drawing on a literature review, expert interviews, and a stated choice experiment (SCE) analyzed through discrete choice modeling, the study identifies the influence of both operational and socio-demographic factors on shared mobility adoption. Key attributes examined include parking distance, booking time, and arrival time deviation, alongside variables such as age, gender, and community belonging. Results reveal that shorter booking times, reduced parking distances, and minimal arrival time deviations increase the likelihood of selecting shared mobility modes over private cars. However, socio-demographic effects were less consistent, challenging common assumptions about their role in mode choice. The findings provide actionable recommendations for municipalities and mobility providers, including tailoring shared mobility solutions to context-specific needs and improving usability to encourage adoption. This research contributes to the shared mobility literature by focusing on SME contexts while also offering methodological
insights for stated choice experiment design in localized urban mobility studies.

The rise of hybrid work in the Netherlands has increased the number of meetings where some participants are remote and others on-site. In agile software development, this change affects high-interaction processes such as requirements elicitation. While prior research addresses hybrid collaboration in general, there is limited insight into how it specifically impacts requirements elicitation. Existing studies mainly focus on tools or broad barriers without exploring how collaboration breaks down when elicitation techniques are applied in hybrid settings. This leaves requirements engineers without structured guidance to address hybrid-specific challenges, risking reduced participation, engagement, and facilitation quality. This thesis applies a design science methodology in five phases: problem identification, design objectives, design and development, demonstration and evaluation, and communication. Literature review and interviews with requirements engineers, product owners, and clients identified three key challenges: unequal participation, insufficient focus, and facilitation difficulty. Based on these findings, three alternative approaches were developed and integrated into a three-pillar intervention strategy: Setup (meeting environment), Structure (meeting design and leadership), and Awareness (participant behavior and mindset). The strategy was tested in three real-world hybrid meetings. Evaluations showed improved focus, more balanced participation, and positive participant reception. The final output is a practical roadmap with three levels of intervention: quick wins, facilitator behavioral adjustments, and collective behavioral adjustments. This research contributes scientifically by identifying hybrid-specific elicitation challenges and offering a layered, tool-independent strategy tested in practice. It contributes to practice by providing a low-barrier approach that does not require major system changes. While results are promising, generalizability depends on context factors such as digital infrastructure, team culture, and facilitation readiness. Future research should validate the roadmap across different organizations and assess its long-term impact.

TeleNeonatology is an emerging digital health innovation that connects hospitals with limited neonatal expertise to specialized Neonatal Intensive Care Units (NICU) via real-time video communication. It holds significant potential to improve neonatal outcomes, reduce unnecessary patient transfers, and alleviate pressure on NICU resources. Despite these advantages, the implementation of TeleNeonatology in the Netherlands remains limited. This thesis investigates the economic considerations that shape such implementation efforts, with a specific focus on how they influence adoption decisions across the different involved stakeholders.
As an empirical entry point, this thesis draws on the 2024 TeleNeonatology pilot between Erasmus MC and Amphia Hospital. The pilot provided operational and economic data. However, the pilot
did not demonstrate measurable clinical improvements, underscoring the need to assess implementation primarily through an economic lens.

The research is structured using the NASSS framework (Non-adoption, Abandonment, Scale-up, Spread, and Sustainability). The NASSS framework provides a structured lens to analyze why health technologies are adopted, abandoned, or fail to scale sustainably. In this thesis, the framework is not only applied to assess TeleNeonatology but is extended with explicit economic dimensions across all seven domains. This adaptation allows economic considerations, such as investment costs, reimbursement models, distribution of benefits, and long-term sustainability, to
be systematically examined. By operationalizing economic questions within each domain, the framework becomes a tool to reveal where misalignments occur between cost savings at the
system level and financial incentives at the institutional level. This integrative approach enables the study to move beyond traditional cost-effectiveness analysis and to uncover how economic logics interact with institutional structures in shaping adoption decisions.

This thesis employs a mixed-methods explanatory design to investigate the economic dimensions of TeleNeonatology. Three complementary methods were combined: a systematic literature review to synthesize existing knowledge, a cost minimization model to analyze economic dynamics in a structured way from different perspectives, and semi-structured stakeholder
interviews to explore institutional perspectives and decision-making rationales. The unifying element is the NASSS framework. This ensured that each phase of the research addressed not
only its own objectives, but also contributed to a coherent overall analysis of how economic factors interact with the organizational, and systemic conditions of implementation. By structuring the
study in this way, the thesis provides both breadth and depth: breadth through systematic coverage of economic considerations across the seven NASSS domains, and depth by combining quantitative modeling with qualitative insights.

The systematic literature review reveals that while high initial investment costs, reimbursement uncertainty, and unclear long-term funding models are frequently cited as barriers, they are rarely
analyzed in direct relation to actual implementation outcomes. Economic considerations are often treated as abstract constraints rather than operationalized decision criteria, creating a disconnect
between economic evaluations and real-world adoption processes. Furthermore, most studies address only isolated aspects within a single NASSS domain rather than examining the interdependencies across domains. Notably, the Organization and Adopter System dimensions receive the least systematic attention, indicating that research to date has insufficiently explored how institutional arrangements and stakeholder dynamics shape the economic feasibility of telemedicine implementation.
The cost minimization model indicates that TeleNeonatology reduces average costs per patient across all perspectives considered, with substantial savings for both the healthcare (€3,940) and societal (€4,081) perspective. These gains derive primarily from avoided NICU admissions, fewer inter-hospital transfers, and mitigation of indirect family burdens. While such results highlight the efficiency of TeleNeonatology in reducing resource utilization, their interpretation differs when viewed from a hospital perspective. In the Dutch system of specialist medical care, hospital services are reimbursed through a case-based payment model, where each treatment trajectory is declared as a billable product at regulated or negotiated tariffs. For insurers, these payments constitute expenditures, whereas for hospitals they represent revenues. Accordingly, modeled cost differences that appear as savings at the payer or system level should be reinterpreted as revenue changes resulting from shifts in the volume of billable products at the hospital level. In other words, the same model outcome that evidences cost savings simultaneously signals income reductions for individual providers, underscoring how interpretation shapes the economic meaning of results across perspectives. This reflects the so-called wrong pocket problem, whereby the actor that bears the costs of implementation is not the one that accrues the financial benefits. Therefore, interpretation strongly shapes the economic meaning of results across perspectives.
The stakeholder interviews corroborate this misalignment. Most institutions operate with only partial visibility over the broader cost-benefit dynamics, and no single actor possesses a full, system-wide economic overview. Decision-making remains fragmented, with hospitals, insurers, and policymakers each prioritizing their own budgetary constraints. This siloed perspective leads to structural misalignments between where costs are incurred and where benefits accrue. Overall, this thesis concludes that economic considerations are necessary aspects for sustainable telemedicine implementation, but are often misunderstood or overlooked in practice. The intervention demonstrates efficiency at the system level, yet its benefits are unevenly distributed and often translate into financial drawbacks for the very providers expected to adopt it. By applying an economics-extended NASSS framework, the study shows that the critical barriers lie in organizational and system-level arrangements, where existing funding structures fail to align incentives across actors. The resulting paradox is that a demonstrably cost-reducing innovation risks stagnation unless governance and financing models evolve to match system-wide value with institutional sustainability.

Based on the generated insights the following recommendations from an economic standpoint should be taken into consideration when implementing TeleNeonatology:
1. Establish structured stakeholder alignment from the start
Misaligned incentives and fragmented perspectives are key barriers. Implementation should begin with a formal steering group including hospitals, insurers, parent organizations, and policymakers. This group should define shared objectives (e.g., reducing transfers, improving parental experience, cost containment), meet regularly, and maintain transparent communication (e.g. with dashboards) economic and financial outcomes.
2. Embed economic evaluation directly into pilot design
Pilots should systematically track economic outcomes across key stakeholder perspectives (hospital, insurer, parental, system). When implementing models, not every perspective must be explicitly included, rather, it is the interpretation of results that determines their relevance. A model showing societal savings, for instance, may simultaneously imply revenue losses at the hospital level. To avoid siloed evaluations particularly those limited to the societal perspective, pilots should explicitly incorporate mechanisms for cross-perspective interpretation, ensuring that economic evidence is actionable and supports informed implementation and scaling decisions.
3. Develop sustainable financial mechanisms to resolve the “wrong pocket problem”
Under current reimbursement structures, avoided transfers reduce hospital revenues even when the system saves costs. To prevent this misalignment, mechanisms should be tested that redistribute savings fairly, such as shared-savings contracts or per-consultation reimbursements. One concrete option is an insurer-funded pool: hospitals would annually report avoided transfers and, if TeleNeonatology led to a net revenue loss, receive compensation up to the breakeven point. This ensures hospitals are not penalized for enabling system-wide efficiency and creates a sustainable basis for broader adoption.

Future research based on this thesis could focus on scaling TeleNeonatology beyond pilot settings including the defined recommendations, and enabling longitudinal evaluation of financial outcomes across diverse hospital networks. In addition, applying the adapted NASSS-economic framework to other telemedicine domains can validate its broader relevance and support comparative insights across digital health innovations. While grounded in the Dutch healthcare system, these insights are relevant for other telemedicine applications and countries, provided that local financial and institutional conditions are carefully taken into account.

Policy-makers in Dutch passenger rail face deep uncertainty in long-term demand forecasting as population growth, urbanisation, and climate change undermine the reliability of traditional models. This research applies Exploratory Modelling and Analysis (EMA) and Multi-Objective Robust Optimisation (MORO) to better capture uncertainty and identify fare policies that remain effective across a wide range of future conditions. Using a simplified elasticity-based simulation model and a multi-objective evolutionary algorithm, the study evaluates thousands of scenarios to explore how fare strategies perform against conflicting objectives (ridership, revenue, CO₂ emissions, and capacity). The results show that extreme fare policies are not robust: eliminating fares boosts ridership and lowers emissions but causes unsustainable revenue losses, while high fares secure revenue but suppress demand and climate benefits. Instead, hybrid strategies emerge as more balanced and resilient. For example, an affordable flat-fare travel pass (akin to Germany’s €49 Deutschlandticket) combined with modest peak-hour surcharges can significantly increase ridership and cut emissions while maintaining financial viability. A long-term analysis (2024–2070) further indicates that no single static policy remains optimal; adaptive fare pathways are needed as conditions evolve. Robust policy trajectories generally feature reduced base fares coupled with a rush-hour surcharge to manage capacity and funding. This adaptive, exploratory approach shifts focus from predicting a single future to preparing for many possible futures, supporting more resilient and sustainable transport policy decisions.

Current food systems cause significant negative impact both environmentally and socially. In response, food hubs have emerged as a potential solution towards a more sustainable food system. By directly linking producers to consumers, food hubs have the potential to create localized food systems, which can enhance both environmental and social sustainability. However, the concept of food hubs remains poorly defined and its exact contributions to social sustainability in specific are not yet fully understood.

Therefore, this research addresses the question “What would an ideal socially sustainable food hub in Amsterdam look like, based on literature and (best practices of) existing food hubs in the Netherlands?”. Using a combination of literature review, interviews and a co-creation session, this study explores the current contribution of food hubs to social sustainability and envisions an ideal socially sustainable food hub in the H-buurt, Amsterdam Southeast.

Findings reveal that food hubs differ in their social sustainability impact depending on their food hub type and position within the food chain. Most significant impact is made at the interplay of producer and consumer. In the context of the H-buurt, residents envision a food hub that incorporates both local food production with the redistribution of surplus food. The hub is led by the community and there is a strong emphasis on fostering social cohesion. The insights regarding the ideal food hub can be used in the further development of a food hub in the H-buurt.

Context and Problem Statement As financial crime grows more complex, governments have tightened Anti-Money Laundering (AML) regulations, with banks serving as the first line of defense. The Know Your Customer (KYC) process, used to identify and monitor clients, is central to this effort. To keep up with the resource intensive process, banks are beginning to turn to machine learning (ML), hoping to improve detection while lowering costs. However, this shift raises new concerns: opaque algorithms may introduce bias, reduce transparency, and unintentionally exclude vulnerable groups from the financial system. In the Netherlands, recent reports have shown how well-intentioned AML measures can lead to discriminatory practices. As the EU prepares to implement the new AML Regulation (AMLR) in 2027, which will further tighten compliance. While policy and technical discourse continues, there is little understanding of how these changes affect society as a whole. Research Gap and Question The literature showes that while AML policies and the integration of ML have been widely studied from regulatory, technical, and institutional perspectives, their broader societal effects remain underexplored. Most research focuses on costs and benefits for banks and governments, mostly neglecting the indirect impact on society. This thesis addresses that gap by systematically analyzing how ML integration into KYC processes shifts the societal costs and benefits of AML compliance. While most evaluations of ML in finance focus on technical or regulatory performance, this research applied a qualitative Social Cost-Benefit Analysis (SCBA) to examine broader social consequences such as exclusion, inequality, and institutional trust. By analyzing current practices and exploring how ML changes these dynamics, the study contributes to a more balanced understanding of innovation in financial compliance. The main research question was therefore formulated as follows: “How does the implementation of machine learning in the KYC process alter the relevant social costs and benefits of the current KYC process under AML regulation?” Methodology This thesis applies a qualitative SCBA framework (Romijn and Renes, 2013) to explore the societal effects of AML regulation through the KYC process, with a specific focus on the impact of ML. The research followed the first five SCBA steps as outlined in Dutch policy guidelines (Romijn and Renes, 2013), including problem definition, establishing a baseline, defining the policy alternative, identifying social effects, and identifying costs. The social effects and costs were not quantified in this research due to the qualitative nature of the identified effects. Due to data limitations the final three SCBA steps were excluded. Primary data was collected through 9 semi-structured interviews with experts from the corporate sector, academia, and human rights advocacy. Interviewees were selected based on their expertise in AML, KYC, and/or ML, and their ability to reflect on broader societal implications. A preliminary KYC process diagram and stimulus texts based on literature were used to structure and enrich the interviews. Interview data was analyzed using thematic analysis in ATLAS.ti, following the six-step method of Willig and Rogers (2017). Emerging codes were grouped into themes and translated into nine key social effects, e.g. inequality, crime reduction, and consumer surplus. These effects were then visualized in a conceptual model showing their interrelations and the impact of ML. Triangulation with existing literature was used to enhance validity. Key Insights The following key findings highlight the most important insights regarding the social costs and benefits of the KYC process and the impact of machine learning within AML compliance. • The KYC Process Has Dual Social Impacts ii iii This thesis examined the social costs and benefits of KYC processes under European AML regulation. Using thematic analysis of expert interviews, the study identified a wide range of interconnected social effects. The candidate positive effects include reduced crime and improved government financial health. Identified potential negative effects include increased administrative burdens on consumers, reduced economic participation, and rising inequality. • Machine Learning Amplifies Both Benefits and Harms The introduction of ML into the KYC process intensifies many of these effects. For the benefits, ML can improves the detection of financial crime and may enhance the efficiency of public enforcement and private compliance. The identified harms that ML can raise were opportunity costs due to legal uncertainty, introduces privacy risks, and may increase stress among consumers. • The KYC Process can Intensify Social Harm and Unequal Burden Distribution A central finding is that society bears the weight of the costs, even though financial institutions and governments control the design and implementation of AML systems. ML expands the surveillance function of banks, blurring the line between private and public roles and potentially reducing accountability. Vulnerable groups can be disproportionately affected through exclusion, algorithmic bias, and stress. This points to a structural imbalance in who benefits from technological innovation and who suffers from its unintended consequences. Conclusions This thesis explored how machine learning reshapes the social costs and benefits of KYC processes under AML regulation. Based on expert interviews and qualitative SCBA, the study found that ML amplifies both the benefits, such as improved crime detection, and the risks, including legal uncertainty, exclusion, and inequality. The effects are interconnected, with societal consequences often falling disproportionately on the public. By framing KYC as a socio-technical system, this research highlights the need for ethical oversight and systems thinking. ML introduces not just technological change; it also creates a responsibility to ensure that its effects are fair and beneficial to society as a whole. Actionable Recommendations This study highlights the need for a more balanced and accountable approach to integrating machine learning in AML compliance. Policymakers should move beyond a narrow enforcement focus by embedding social impact assessments into AML policy design and collaborating with academic experts. Regulation must ensure that ML systems are not only legally compliant but also socially fair and explainable. Financial institutions must take responsibility for the real-world effects of ML in KYC, prioritizing explainability, bias mitigation, and transparent vendor oversight. Close engagement with regulators and researchers is key to setting fair and practical standards. Finally, society, though not directly involved in implementation, bears many of the negative impacts. Public awareness, advocacy, and particip

This research explores various mobility measures in the battle with excessive nitrogen deposition. It comprises a case study research of two study areas in the Netherlands with Natura2000. By employing the MOVE Meter as a quickscan tool with traffic modelling, effective local mobility measures have been identified. Establishing a no-entry zone emerged as the most effective measure for the reduction of nitrogen deposition due to its proximity to natural areas, highlighting the significance of spatial factors. This research also revealed modelling constraints related to the implementation of emission factors in traffic modelling.

Public transport operators face significant challenges in maintaining a high Quality of Service, which is essential to encourage a modal shift from private vehicles to public transport. One major factor affecting service quality is crowding. This issue can be mitigated by increasing capacity, for example through the deployment of articulated buses. However, the impact of articulated buses remains relatively underexplored. This study investigates the effects of introducing articulated buses in public transport networks, focusing on their potential to alleviate crowding, address labour shortages, and enhance the passenger experience. Using a mixed-methods approach, both operational and passenger-level implications of deploying articulated buses are examined.

The aviation sector must reduce greenhouse gas emissions. The European Commission introduced the Fit for 55 package and ReFuelEU Aviation regulation to reduce the emissions. Part of ReFuelEU is the obligatory use of sustainable aviation fuels, including synthetic variants (eSAF). eSAF can reduce greenhouse gas emissions by around 90% compared to traditional kerosene. Power2X and Advario have decided to partner up and build the first large-scale eSAF production facility in Rotterdam by 2030, producing 250,000 tons of eSAF annually. This accounts for 40% of the needed European supply based on ReFuelEU aviation blending obligations. Currently, the feasibility of these large-scale production facilities remains uncertain due to high production costs, immaturity of hydrogen and eSAF production technologies, and unclear regional (national) policy support that does not align with international policies. Existing research emphasizes the need for a systemic change apart from..

The Netherlands has positioned itself as a transit nation within the context of military mobility on the European continent. By making its dense and well-developed infrastructure system available, the country facilitates rapid deployment from its seaports to destinations in neighbouring countries. However, using civilian infrastructure for military transits could potentially cause serious disruptions to the travel time of civilians due to capacity limitations. Recognising this tension, the Dutch Ministry of Defence (DMoD) is responsible for balancing the simultaneous use of transportation infrastructure by civil and military vehicles and seeks interventions in the system that can aid them.

The study follows a three-phase methodology:
1. The system analysis examines the transportation system with influences of military mobility by combining insights on the Dutch infrastructure’s characteristics, military movements, and possible intervention strategies for the DMoD.
2. The model development results in a mesoscopic agent-based model (ABM) in NetLogo that simulates simultaneous infrastructure use by civil and military vehicles on synthetic Dutch-like infrastructure networks.
3. The experimentation tests different intervention strategies by simulating their effects on military transit time and travel time loss in the transportation system.

The simulation experiments show that using trains and barge ships is very effective in minimising travel time loss for civilian vehicles, but results in a longer duration of the military transit operation. Combining the road, rail and inland waterway modalities, applying short intervals between vehicles and using long road convoys, are effective in minimising that duration, but cause more travel time loss. The combination of these latter interventions with the avoidance of rush hours and/or daytime balances the trade-off between travel time loss and transit time. Additionally, operating short road convoys should be avoided as they serve neither of the two goals and reserving roads for military vehicles only does not significantly change either of the two goals. This differentiation stresses the importance of setting out the intent of operations - minimising travel time loss or the transit time - before planning them.

The sensitivities and limitations of the research, however, should be kept in mind when interpreting these conclusions. Especially the assumption that the speed flow of highways is capped at 80 km/h on two-lane roads when there are military convoys, strongly affects model outcomes. Other limitations are that the research neglected traffic congestion caused by accidents and road works, and the availability of transport vehicles. Microscopic research on the interaction between civil vehicles and road convoys is suggested to gain insights into these parts of the system.

In addition to partly filling the knowledge gap of simultaneous infrastructure use by military and civilian vehicles, this research also contributes to the scientific fields of managing multi-modal transport and traffic and modelling of transportation systems. Regarding multi-modality, four different management categories were studied, of which three are concluded to be effective: traffic demand management, congestion avoidance, and integrating multiple transport modalities. Prioritisation of roads for military vehicles, however, did not result in significant system-wide benefits in this context.

In the field of transportation modelling, this research confirms the applicability of Agent-Based Modelling (ABM) to this field and introduces a network generation algorithm used before the transportation simulation. The benefits of this algorithm are that it requires less data about the network, which was useful due to the sensitive character of this research topic, as well as a broader applicability of the model, because adjustments in the network generator allow simulation in a different context.

This thesis investigates the strategic positioning of protective coatings firms in the emerging European hydrogen pipeline market. With Europe’s transition to net-zero, hydrogen is becoming a pivotal enabler of decarbonization across various sectors. However, existing pipeline infrastructure requires significant adaptation, and coatings firms face challenges in developing consistent strategies for entry. This study develops a Market Entry Framework designed to guide coatings companies through the complexities of this evolving market.

A qualitative abductive methodology was employed, combining literature analysis, market data, and expert interviews to construct the framework. The findings identify key internal capabilities, technological innovation, regulatory adaptability, and relational positioning, as critical factors for successful market entry. Insights from the market data analysis confirm high growth potential for hydrogen-compatible coatings, with regional concentrations in Germany, France, and Benelux.

The framework consists of six interconnected dimensions, Motives, Entry Barriers, Contextual Factors, Firm Capabilities, Strategic Entry, and Outcomes. It provides practical guidance for firms, emphasizing the importance of early regulatory engagement, internal coordination, and iterative strategic approaches. Ultimately, the framework supports firms in navigating market timing, regulatory complexity, and internal readiness, while positioning themselves for long-term success. The thesis also contributes to the broader field of strategic management by demonstrating how Resource-Based Theory can be operationalized in uncertain, highly institutionalized markets.

This study investigates the impact of a ban on short-haul flights in Europe on transfer passengers and CO2 emissions, focusing on both time-based and distance-based bans. Employing a route assignment analysis of a base scenario of Europe with an optimal High-Speed Rail (HSR) netwerk, compared with various scenario's of a ban. Results reveal that the impact of the ban on CO2 emissions is influenced by transfer passengers, primarily due to their average detour distance. The effects on different European hubs vary based on their geographical and operational characteristics. The findings suggest that a time-based ban could contribute to CO2 emission reductions, but more research is needed on the effects of the ban within the current HSR network in Europe. It is clear that the ban will impact hubs, and future research should examine how this will affect connectivity and economies in different regions.

This report presents the development and application of a standardized methodology for assessing the accessibility of multimodal public transport systems. As cities around the world face growing pressure to deliver efficient, inclusive, and sustainable mobility, there is a clear need for analytical tools that can evaluate how well transit networks serve diverse populations across both space and time. This is particularly important in multimodal systems, where the overall user experience depends not only on the performance of individual modes, but also on how well these modes are integrated in terms of spatial layout, service availability, and schedule coordination. Although many existing studies have evaluated public transport accessibility, most of these focus on single modes or static indicators such as travel time and coverage. They often neglect the dynamic, interconnected nature of real-world transit systems, and provide limited insight into how multimodal integration affects system performance. To address these gaps, this research introduces a comparative framework that combines spatial and temporal accessibility metrics using publicly available GTFS data and graph-based network representations. The primary objective of this research is to design a methodology that enables structured, comparative analysis of multimodal public transport networks. The study applies this methodology to a diverse set of twelve cities and investigates whether the resulting metrics reveal meaningful insights into the spatial and temporal structure, coordination, and operational strategies of their networks. These insights then serve as the foundation for identifying system strengths and weaknesses and developing targeted policy recommendations.