The global wicked problem of the climate crisis has drawn significant interest and attention over recent years from world governments, industry leaders and researchers. In a collaborative effort to tackle this problem while maintaining economic and social development, in 2015 the United Nations developed the Sustainable Development Goals (SDGs) and countries globally committed to this initiative (United Nations Development Programme, 2024). The transport sector forms a crucial component of infrastructural development while also being a significant contributor to greenhouse gas emissions, and a sizeable share of these emissions are attributed to the use of Internal Combustion Engine Vehicles (ICEVs), dependent on petroleum fuels. The globally recognised solution to replace ICEVs as of 2024 is through electrification of the transport sector, facilitated by the use of Electric Vehicles (EVs). Given the importance and significance of this relatively emerging technology, countries across the globe have formulated various strategies and made major efforts to enhance their country’s competitive positioning in the international trade of EVs.

Enhancing competitiveness in international markets in the EV sector is a viable method for nations to improve their energy security and independence from other potentially rival countries for their energy requirements. For instance, in the aftermath of the Russia-Ukraine conflict, many countries minimized ties with Russian products, despite Russia being the world’s largest exporter of oil to global markets, further stressing upon the need for countries to strengthen their positions in electric mobility and EV trade, since the incumbent car companies that built their reputations on ICE vehicles have also started updating their vehicle line-ups and incorporating EVs due to the constantly evolving nature of the transport sector. Moreover, the government support through industrial policy subsidy measures directed towards their domestic industries to bolster EV production also communicates concerns about employment for incumbent ICE vehicle producers, as with the growth of adoption in any emerging technology, or EVs in this case. Hence, the continuously evolving trade competitiveness dynamics require focused research to support policymakers in recognising the factors that may have a positive or negative impact on EV export performance. This can further facilitate the introduction of effective policy measures to improve international competitiveness in a sector such as EVs, which is of paramount importance in the widespread uptake of Net-Zero technologies.

There is an abundance of research analysing different aspects of the adoption and uptake of EVs in global, national and regional settings. Additionally, the extant literature also contains works that analyse the determinants of international competitiveness in established industries such as agriculture, forestry and the automotive sector. However, there is a lack of research on the international competitiveness of countries in the EV sector. Hence, the primary objective of this study was to fill this knowledge gap by providing a cross-country, empirical analysis of the determinants of international competitiveness in the EV sector, such as technological innovation, cost-competitiveness, green industrial policy measures and endowments in critical metals required for EV manufacturing. The following main research question guided this study: Which factors are most strongly associated with the export competitiveness of the electric vehicle industry across countries?...

Cyclist route choice can lead to uncomfortable and dangerous situations. Therefore, it is important to research ways to influence this. This study explores how infrastructural nudging can be used to influence cyclist route choice. Using virtual reality and a bicycle simulator the impact of visual road hierarchy, visual obstruction and herding through street art is studied. A virtual urban environment was designed with 11 T-intersections. In 9 of the 11 intersection these methods were applied in three different ways, to see whether the nudge impacted cyclist route choice. The gathered data was then analysed with descriptive data analysis and discrete choice modelling. This study shows that cyclists follow nudges using visual road hierarchy and herding. However, they do not follow nudges with visual obstruction. This effect is the same for people of all ages, genders, heights etc. Though less experienced cyclists seem to react more heavily to obstruction methods. The results also indicate that cyclists have a significant right-handed tendency. This effect is not influenced by eye or hand dominance. The effect becomes slightly weaker when nudging is applied but does not go away. Future research should validate these results in a physical environment before this is used in practice.

Disruptions in urban public transport networks significantly impact urban mobility, causing inconveniences for passengers, economic losses, and vulnerabilities in transit systems. Major disruptions may lead some passengers to permanently switch to alternative modes like cars, even after the disruptions resolve, potentially worsening congestion and environmental issues. While the immediate impact of disruptions on behavior has been extensively studied, there is limited research on the long-term effects, often restricted to qualitative methods. This study aims to address this gap by proposing a framework to investigate the prolonged effects of public transport disruptions on passenger travel behavior using smart card data. A mixture latent Markov model is used to track passenger behavior from the pre-disruption to the post-disruption period. This framework identifies travel patterns and tracks how passengers transition between these patterns over time, thus inferring the impact of disruptions on behavior. Each passenger is assigned a mobility style that reflects their general travel attitude, such as those who do not change their behavior. The results from our case study reveal that the impact of the disruption was not as substantial as anticipated, with a high proportion of passengers maintaining their behavior.

While the romantic appeal of historic, centrally-located railway stations is undeniable, their efficiency in accommodating high-speed rail (HSR) services for passengers is questionable. The allocation of centrally-located stations benefits passengers who start or end their journeys in major cities, but imposes longer travel times for traversing passengers because trains generally reduce their speeds in urban environments. The majority of Europe’s HSR-stations are located in these urban centers, where expansion options are severely restricted. Therefore, this study investigates the passenger welfare effects of relocating HSR-services from city centers to city outskirts, which could be an alternative to capacity expansion in existing stations. It does so by utilising a combined route-choice model, with the Trans-European Transport Network (TEN-T) policy for 2050 as input. This study forecasts the station bottlenecks for 2050 and examines various HSR-service relocation scenarios. A logsum-based economic appraisal method evaluates the impacts on passenger welfare of these relocations. The findings reveal substantial potential welfare benefits when HSR services are relocated to peripheral stations. The order of magnitude of these benefits to the European economy ranges from tens of millions of euros per year to a couple of hundred million euros per year. This range depends on the traversing passenger volumes and the comparative in-vehicle time savings and distance savings between peripheral and urban routes. Both of these savings originate from the most important predictor of passenger welfare effects: the network design. It is recommended to policymakers to not instantly dismiss the possibility of peripheral HSR stations from a protectionist point of view, but to consider the option for its relatively low construction costs and externalities, and especially the potential for substantial passenger welfare effects.

The reimbursement scheme in geriatric rehabilitation (GR) contains stepwise tariffs depending on both the length of stay and treatment intensity: after a pre-determined number of inpatient days or treatment hours, the total reimbursement jumps to a higher level and then stays constant until the next threshold is reached. These discontinuous reimbursements may distort physicians’ behaviour in a way that is detrimental to the efficiency of care provision. This thesis examines whether these behavioural distortions are present regarding GR and, if so, which factors are associated with stronger distortions on a GR provider level. For this goal, GR claims data from health insurer CZ covering the years 2017–2023 are used. Furthermore, this thesis qualitatively assesses the possible consequences of the proposed changes in the payment system of GR, including the abolition of the current stepwise tariffs, using insights from GR claims data and economic theory.

This thesis explores the impact of transport disadvantage on the unemployed's participation in out-of-home activities beyond the labor market. It delineates eight distinct groups primarily based on transport accessibility and car availability among the unemployed, assessing how these factors are shaped by socio-demographic characteristics and influence engagement in various activities, such as shopping, social visits, and recreational activities.

Latent class cluster analysis is employed to identify distinct patterns of transport accessibility and car availability among the unemployed, avoiding arbitrary definitions and incorporating socio-demographic determinants. This approach reveals a broad spectrum of transport accessibility and car availability patterns, ranging from low to medium to high, organized into groups or clusters.

The research contrasts out-of-home activity participation between unemployed and employed individuals with similar socio-demographic profiles to identify relative deficits, using propensity score matching. This method isolates the combined effect of employment status and non-socio-demographically determined transport disadvantage on activity participation and travel behavior. The study then conducts a comparative analysis of deficits in activity engagement across the eight groups, allowing for the separation of the direct impact of transport disadvantage from direct effects related to employment status.

A key discovery of this thesis is a compensatory mechanism among unemployed individuals who tend to increase their participation in non-work-related activities. However, this compensatory behavior is not uniformly realized across all groups, particularly not among those with low car availability, who exhibit significant deficits in out-of-home activity participation. Surprisingly, accessibility does not seem to influence the differential in activity participation between the unemployed and their employed counterparts, suggesting that car availability is the primary dimension through which compensatory behavior is enabled.

This finding challenges the hypothesized compensatory relationship between transport accessibility and car availability and suggests that individuals with higher car availability, regardless of their level of accessibility, are at a lower risk of transport-related social exclusion, whereas those with low car availability exhibit a higher risk, irrespective of their accessibility levels.

Furthermore, the results suggest that socio-demographic factors compound with employment status and transport disadvantage to inhibit the full expression of this compensatory mechanism among low car availability groups. These groups, representing 61% of the unemployed sample, typically have higher accessibility and comprise younger people, individuals living in single-person households, those with lower household incomes, and individuals with a non-native (parental) birthplace, predominantly residing in highly urbanized areas.

This study, informed by existing literature on affordability limitations, recommends enhancing affordable transport accessibility for unemployed individuals with low car availability, advocating for subsidized public transport fares and travel allowances for low-household-income groups. It supports promoting mixed-use developments and enhancing infrastructure for active transportation modes to increase activity options within reachable distances for those with limited car availability. Future research should explore physical and digital in-home activities, subjective experiences of social inclusion, and the role of transport and housing expenses in shaping transport affordability.

This research aims to enhance the understanding and quantification of logistics in the Netherlands by identifying factors influencing freight activities in urban areas. Using a linear regression model developed from BasGoed freight tour data, CBS location data, and Dutch zonal plan data, the study integrates 26 variables to predict freight trip generation and freight generation. The main explaining factors include the surface area of distribution centers, employment in various sectors, the presence of rail or inland waterway terminals, urban density, and the surface area of business spaces. The model, validated through statistical tests and literature comparisons, provides reliable predictions, with employment influencing freight production and surface area affecting freight attraction.

A case study in the Utrecht A12 zone demonstrated the practical relevance of the model, offering insights for policymakers to design logistics-friendly urban environments. Limitations include simplified statistical procedures and reduced accuracy in seaport and transhipment areas. Despite these constraints, the model delivers valuable predictions for various spatial scenarios.

Future research should explore model performance using CBS XML-microdata, consider non-linear relationships, and examine variables for shipment size and vehicle type. Addressing causality and endogeneity with instrumental variables is also recommended. This study contributes significantly to both scientific and societal domains, providing a robust model for freight trip generation and freight generation, and guiding municipalities in effective spatial planning and decision-making for sustainable urban development.

The primary objective is to explore the relationship between support for Universal Basic Income (latterly referred to as UBI), labor participation, and poverty reduction in the Netherlands across various financial policies. The research also seeks to examine the behavioral responses of Dutch citizens to UBI policies, considering potential benefits beyond mere financial aspects such as education, health, and volunteerism. Ultimately, the study aims to offer policy recommendations regarding UBI based on its findings.

To address these objectives, the study analyzes the impact of UBI on recipients' disposable income. Support for UBI and its influence on labor participation are investigated using conjoint analysis, which involves varying attributes such as the value of UBI per adult, UBI per child, changes in the taxation system, and modifications in allowances. Additionally, the study employs a traditional survey to gauge respondents' intentions regarding the use of time and money under two proposed UBI policies: mid-UBI and high-UBI. Furthermore, the effect of UBI on poverty is assessed by examining marginal taxation rate tables from prior research, testing five different UBI policies to determine their impact on household incomes and changes in marginal tax rates.

The findings of this study reveal mixed effects on both labor participation and support for UBI, with an average of a slight overall increase. However, there is variability in the results, with both positive and negative values present within the confidence intervals. Notably, single parents and individuals receiving welfare payments demonstrate increased labor participation across their entire confidence intervals, despite lacking support for UBI. This discrepancy can be attributed to the fact that single adult households and single parents tend to fare poorly under UBI, facing heightened poverty risks. Conversely, UBI appears to be beneficial in boosting disposable income for multi-adult households, positioning them as the primary beneficiaries of this policy.

To ensure the success of UBI, a shift in policy is imperative, moving away from incentivizing individual living— a trend that has been increasingly prominent in the Netherlands. As an initial step, UBI can be introduced modestly with a taxable monthly amount of 210 euros per month per adult, supplemented by 300 euros per month per child, and bolstered by the provision of rent allowances and free childcare. This approach aims to augment the disposable income of households earning up to 60,000 euros in gross income, without adversely affecting higher-income earners. Notably, this policy adjustment also serves to reduce marginal taxation rates from levels sometimes exceeding 100\% to a more manageable 69\%.

To progress towards higher UBI values per adult, further research is warranted into inefficient tax deductions, potential increases in income tax, considerations around land value and property tax, and the reallocation of governmental employees. Based on the findings of these investigations, UBI could be incrementally increased to 630 euros per month per adult, at which point the rent allowance may be phased out.

At the threshold of 630 euros per month, significant enhancements in quality of life are expected to ripple across the population. These improvements encompass a reduction in stress levels, augmented accessibility to healthcare services, the empowerment to opt for healthier dietary options, and an enhanced ability to save among low and middle-income earners in the Netherlands. Building on these gains, the trajectory of UBI can be extended towards a higher benchmark of 1200 euros per month per adult. This upward progression aims to unlock additional benefits, including a more optimal allocation of jobs within the Dutch populace and further mitigation of marginal taxation rates towards levels akin to standard income tax rates.

In today’s world, the transport system is essential in helping people reach the activities they want or need to attend. However, like in many other fields, inequality can exist in access to various opportunities based on personal characteristics. Little is known about how gender and intersectional factors impact people's perceived accessibility. This thesis aims to fill this knowledge gap using a mixed methods approach. First, desk research shows that perceived accessibility can be influenced by a large variety of factors, including, but not limited to, the primary mode used, perceived safety in transport and socio-demographic factors. Second, Structural Equation Modelling is used to investigate the strength of relationships between the various found factors and gender intersectional factors. This analysis shows that being a woman has a negative effect on perceived accessibility, which can for a part be explained by women feeling less safe in transport at night. Additionally, the analysis shows that for women having young children negatively impacts perceived accessibility, but not for men, while for men income positively relates to access to a car and in turn to perceived accessibility, an effect which is not found for women. Finally, the subsequent qualitative analysis, using workshops, shows that little attention has been paid in the past to (social) safety issues in transport and to differences in needs between different kinds of travellers. In the future, more cooperation between transport institutions and integrated intersectional policymaking in transport would be of significant value.

Accessibility is a fundamental concept concerning urban and transport planning as it is the elementary basis for socio-economic development in cities. It can be described as the potential to reach spatially distributed opportunities. Recent research has identified that excluding person-based features from this analysis might cause inaccurate measurement of accessibility. Age, gender, and income, for instance, are responsible for varying accessibility levels drastically. In addition, studies worldwide have shown that women face different challenges in reaching locations and spatially distributed opportunities. Despite the findings, this research identifies a gap in understanding how a person-based perspective, mainly gender, and other personal characteristics, affect accessibility levels, considering various travel purposes and transport modes. Thus, this research aims to answer the question, ”How do person-based characteristics, mainly gender, can impact accessibility levels?”. The primary objective is to explore how these characteristics influence accessibility metrics, identify the urban groups most affected by the absence of this perspective, and determine the key personal characteristics that significantly impact accessibility levels. To address these objectives, this study considers a combination of quantitative and case-study research approaches. It investigates the Metropolitan Region Rotterdam- The Hague in The Netherlands by applying surveys to obtain perceived accessibility data. In the same region, spatial analysis is conducted by mapping transport networks and points of interest. Then, this study compares spatially calculated accessibility with self-reported accessibility and the presence of mismatches. In addition, cluster analysis identifies the urban profiles most vulnerable to mismatches and their main characteristics. A Binary logistic regression is conducted to determine the variables’ importance in the mismatch occurrence. From the survey answers, it is identified that women have less access to cars than men. In addition, the comparison between perceived accessibility and spatial accessibility uncovers that women present the most critical mismatches to reach activities by car. In other words, several women perceive the car as an impractical option to access points of interest that are spatially considered reachable by car. It raises the hypothesis that the lack of car access highly impacts their accessibility perception. Furthermore, the clustering analysis reveals that foreign women exhibit a higher prevalence of car-related mismatches when compared to other urban groups. Moreover, this research identifies that fathers of young children also encounter greater disparities across all transportation modes. Additionally, the binary logistic regression underscores the importance of safety as a critical factor influencing women’s perception of walking as a viable mode of transportation. This safety importance is also identified from the survey answers. The findings raise new hypotheses that warrant investigation in transport engineering, urban planning, and social sciences. Consequently, this research can contribute to developing more inclusive transport policies and establishing a more equitable transport system.

Passenger traffic by car is regarded as one of the main contributors to energy consumption and emission in the transport sector. Car dependency and limited shifts to more carbon-friendly alternative travel modes in industrialised countries play a major role in maintaining unsustainable mobility systems, despite governments' increased attention and effort in enhancing multimodal travel behaviour. In other words, having a diverse mode usage as a travel user. Compared to travellers who only use the car, multimodal travellers are likely easier to shift to more sustainable and health-enhancing modes, such as the bicycle and public transport, when applying policies. However, how (multi)modal travel patterns developed over time and the determinants of being multimodal are not often researched in combination with measuring multimodality and showing corresponding travel patterns. A Latent Class Cluster Analysis is performed once for 2010-2017 using cross-sectional data from the Dutch National Travel Survey (OViN) to measure multimodality whilst capturing distinct travel user groups per year, based on the frequency of travel mode use. Socio-demographic, mobility resource and built-environment variables are included as potential determinants of belonging to a specific group. The main results are that overall mobility patterns of the captured travel user classes were hardly subject to change. Moreover, only the smallest two out of five identified classes have travel behaviour with a higher degree of multimodality. Besides, the likely strong effect of owning mobility resources or not (e.g., a licensure, household car, company car, or household bicycle) on being likely in a car-dependent or a multimodal travel user group is shown. Most remarkably, our findings add to the existing knowledge by revealing that company car ownership plays a significant role in being a car-dependent travel user. Based on our results, identified policy directions include, but are not limited to, affecting mode choices of employees (owning a company car) via employer-based programs to incentivise them to use the bicycle or public transportation. Nevertheless, the knowledge about several travel user classes comprising multimodal travel patterns can be extended in several areas, most notably, by including attitudinal factors which could be tracked longitudinally on the individual level, such as perceptions about willingness to use travel modes, to acquire a more profound view about what strives people to behave in a certain way over a more extended period.

With finance entering the real economy, new kinds of markets have emerged, such as futures markets, in which special contracts – called futures – are exchanged. From the early 2000s, futures on commodities have become popular among financial instruments, and we talk about "financialization of commodity markets". The uniqueness of commodity futures markets is that they see the participation of two structurally different market actors: physical commodity participants and speculators. Several, opposing claims have been made about speculation within commodity futures markets. One important claim is that the increase in speculative activity has, in certain periods of time, driven up commodity prices over and above the levels warranted by fundamentals of demand and supply.
This thesis investigates the role of speculation on the prices of two food commodities, corn (maize) and soybeans, and answers the following research question: To what extent did financial speculation have a role in rising food commodity prices over the years 2004-2023? To do so, a "multi-method" approach is employed. Specifically, the four methods are: a literature review; a replication of an econometric model; statistical inspections and linear Granger causality tests; and interviews with experts.
First, the "indirect" effect of speculation on food commodity prices through the oil market is examined with the extension of an econometric model proposed by Knittel and Pindyck. The analysis shows that, in recent periods, speculation can account for around 11% of the oil price changes. Then, through Granger-causality tests, a Granger-causation chain between oil prices, fertilizer prices and food commodity prices is found. The two findings together prove that speculation has some role in affecting oil prices, and that dynamics in oil markets are transmitted to food markets. Second, the thesis proceeds by assessing – though empirical analyses and Granger causality tests – the more direct effects of speculative activities in food commodity markets themselves on food commodity prices, as well as the relation existing between spot and futures prices. Even though the statistical evidence of speculation driving prices is found only in a few specific cases, the chapter concludes that speculation cannot be completely excluded from the factors affecting prices’ dynamics. Eventually, this thesis comes up with a theoretical-cum-empirical framework – justified and sustained by a literature review, a model replication, some statistical inspections and tests, and interviews of experts – that concludes that excessive speculation has – to some extent – been driving up and affecting commodities prices: specifically, speculation has increased volatility in futures markets.

Agile development approaches are becoming more popular the last years. With large success in the software industry, the interest in using agile methods has shifted to the development of physical products. Since agile has been originally developed for the software industry, it does not cover aspects of the hardware development environment. However, some companies do want to switch from a traditional approach to a hybrid or even complete agile approach. Reasons for this can vary from the wish to reduce time-to-market, improve communication and collaboration among the teams, or because they do not want to lack behind their competitors even though they did not know if the method will be effective for their own company. The problem is that the implementation and use of the agile methodology in the traditional approach face some issues concerning effectiveness, performance, and mismatch with expectations. The objective of this research is to look into the implementation and use of the agile methodology in the traditional models in the hardware industry. This has led to the research question: How to effectively implement and use the agile methodology in the hardware environment?

A literature review has been conducted on the success factors and barriers of agile implementation in the hardware environment. Furthermore, the benefits and challenges of the use of agile are investigated and an overview is created. Moreover, a qualitative research approach is used to dive into the perceptions and best practices of agile by doing a case study with multi-person interviews at a large hardware development company. The findings of the success factors, barriers, benefits, and challenges are identified and compared with the literature. Remarkably, the success factors and barriers are difficult to see independently of each other, as they are often linked and can reinforce one another.

The way of implementing and using the agile method must match the company and the people. For the implementation, the mindset of the people should be right in place and the pilot can show if the proposed way of working is right. For the use, it is of significant value that besides the people aspect, the tools, and organizational structure are aligned. Also here, the rituals, prototyping, training, and way of specialization should match the company and the teams. The findings of this thesis can be used as a guideline to implement and use the agile methodology in large-scale hardware companies.

During the influx to mass events in the past, situations have regularly arisen that people experienced as unpleasant or even unsafe. Nowadays, many researchers focus on regulating this influx by influencing travel choices, where only little attention is paid to travellers’ preferences. This research identifies the preferences of different type of visitors towards receiving information, and the extent to which visitors with different information profiles act on the information messages they receive. A survey was distributed (N=378), and the number of variables was reduced by performing an Exploratory Factor Analysis. The Latent Class Cluster Analysis distinguished three clusters based on their information message preferences. The results show that people are most interested in receiving information via email a few days before departure and that it is challenging to inform them at a later time. The most impressionable cluster mainly consists of young people, unfamiliar with the event area. Event organisations can apply the results by better-aligning communication with the preferences of their visitors. The differences in behavioural intentions between the clusters turned out to be limited. This must be further investigated since it is not in line with expectations.

Short-haul flights, known for their high carbon intensity relative to distance, are increasingly challenged by high-speed rail (HSR), a viable and environmentally friendly alternative. This study develops a model adapting the “Transit Network Design and Frequency Setting Problem” to high-speed rail, focusing on optimizing existing networks rather than designing new ones. Two sequential algorithms are developed and integrated in the model, which initially generate the optimal paths in the network, and subsequently, set the appropriate frequencies to the existing and new lines. The model is applied to two scenarios within the European rail network, to assess the impact of network modifications on demand distribution, operational costs, and infrastructure development. The findings reveal that the impact of network modifications varies depending on the size and development level of the area. In smaller, well-developed networks, modifications mainly involve adjusting frequencies of existing services, while larger areas with diverse infrastructure levels necessitate new lines and modified configurations. The model offers varying implementation options, balancing capacity utilization and cost minimization, aiding stakeholders in making informed decisions. The potential of utilizing existing infrastructure for sustainable mobility is highlighted, suggesting that future endeavors should include diverse case studies and incorporate user and social cost perspectives, along with timetabling and operational factors for a more comprehensive evaluation

The Dutch banking sector is mandated to identify and report transactions that may signify money laundering (ML) activities. Banks have been reliant on rule-based transaction monitoring (TM) systems that flag transactions exceeding predefined thresholds. While such systems are instrumental in filtering potential ML transactions, the inherently small prevalence rate of ML occurring in the vast majority of financial transactions causes these systems to produce only a limited number of flagged transactions. Furthermore, as flagged transactions are only those surpassing certain rule thresholds, the alerts are biased toward presumed risk distributions. Consequently, this causes the performance regarding transactions that go unreviewed but should have been flagged, so-called false negatives, to remain unknown. This lack of understanding is a critical gap in the efficacy of current anti-money laundering (AML) controls and motivates the need for better insights into this False Negative Rate (FNR). Addressing this critical need for enhanced discernment of FNR, this study aims to improve this knowledge gap by answering the following research question: ‘To what extent could a supervised machine learning classifier, when trained on historical alerts, assist Dutch banks in estimating the False Negative Rate of rule-based transaction monitoring systems concerning unreviewed transactions?’. To achieve this goal, this study adopts a mixed-methods research design by combining a literature review with seven interviews with domain specialists to acquire insights into transactional ML typologies and the underlying indicators and thresholds employed in existing rule-based TM systems. The study further extended to the development of eight different types of supervised machine learning classifiers, applied both using their default settings as well as with balancing measures in place when possible. These classifiers were trained on two synthetically generated datasets of both 180 million transactions, one with a high and one with a low ML prevalence rate, indicating the relative frequency of ML transactions. These mirrored real transactional patterns in order to evaluate the feasibility of estimating the FNR pertaining to unreviewed transactions utilizing historical data on flagged transactions. In addition to establishing the effect of both different ML prevalence rates on performance, we also explored whether combining data from multiple financial institutions into one shared information perspective could be of additional value. The findings indicate that the classifiers struggle to accurately predict the FNR, especially in scenarios of low ML prevalence and without combining information from multiple institutions. There is a significant discrepancy between the actual (0.729 to 0.988) and predicted FNRs (0.156 to 0.649), even in higher ML prevalence settings. The low performance, as evidenced by poor Area Under Precision-Recall Curve (AUPRC) and Matthews Correlation Coefficient (MCC) scores, highlights the challenges in using machine learning for ML transaction detection in Dutch banking, calling for further research and development of more advanced detection models Despite the restrained success in accurately estimating FNR through supervised machine learning classifiers, the insights derived from this research are of considerable value. They prompt a critical examination of the current TM systems and suggest a pivot toward more sophisticated machine learning techniques for FNR estimation...

The residential energy consumption and energy affordability are crucial in policy design, with regard to energy efficiency and energy poverty. This has become even more relevant now, as the energy transition is taking off and the energy prices are soaring. Given the inequality in the energy consumption and energy affordability, it is therefore critical to know which factors are impacting these phenomena, and how they are impacting these phenomena. The housing-related factors are the least understood factors impacting the energy consumption and energy affordability, even though these factors are key pillars of energy policy. Thus, this research assesses how housing-related factors impact the residential energy consumption and energy affordability in the Netherlands. For this assessment, multiple data sources are combined over the years 2016 to 2018, covering a total of more than 8 million Dutch households. A regression analysis is performed on a postal code 6 level using the housing-related factors as predictors, while controlling for socio-demographic and weather factors. This revealed that the housing-related factors impacting energy consumption and energy affordability are relatively similar. This study finds that the WOZ-value is a strong predictor for both phenomena as houses with a higher WOZ-value have a lower energy efficiency. Furthermore, it shows that rental households consume less energy but spend a similar percentage of their income on energy, and that the impact of the year of construction on both phenomena has increased significantly, when compared to earlier studies. Based on this, it is concluded that rental houses and houses with an older year of construction deserve more attention in Dutch energy policy. Furthermore, low-income households should be targeted in short-term energy policy, as this study shows that the current soaring energy prices have a severe impact on the energy affordability in the Netherlands. Insights are however broader, and can be pivotal in energy demand and energy affordability projections, both in the Netherlands and the rest of Western Europe.

Urban densification is one of the most relevant strategies to reduce the housing shortage in the Netherlands, as it also aims to prevent additional pressure on transportation networks and contributes to more sustainable travel. The effects of the built environment on travel behaviour have been studied extensively. To date, most studies focused on homogeneous effects of the built environment on travel behaviour indicators. Less attention is paid to possible heterogeneity in the effects dependent on other variables. This study applies moderated multiple regression on Netherlands Mobility Panel data to analyse possible heterogeneity of the effects of determinants of travel behaviour on daily travel distance, dependent on the level of urbanisation. The results show that various determinants indeed have effects with varying strengths, dependent on the level of urbanisation. The findings advance the understanding of the relationships between built environment and travel behaviour, which could be used to design tailor-suited policies that efficiently target specific groups. In relation with the built environment, the results show that densification strategies have the desired effects. However, accompanying mobility strategies are necessary to achieve the full potential of urban densification.