The aim of this study was to identify relevant relations between the adoption of electric vehicles at public charging stations and the characteristics of the neighbourhood through a case study in the Netherlands. The study started with creating such measurements of EV adoption in the transaction data of public chargers in the MRA-Elektrisch region. Subsequently, the relevant variables found in the literature study on the drivers of electric vehicle adoption have been translated into a set of neighbourhood characteristics using open data sources. Based on the identified relationships, a further analysis was conducted in order to develop policy recommendation for the municipality. It is suggested that these municipalities increase the capacity of public charging points in high-populated regions, as both the relative and absolute numbers of residents were positively correlated with the occupancy rate. Furthermore, it is recommended to prioritise the neighbourhoods with high potential and a high potential EV user per charger ratio that is high under current conditions for the placement of additional chargers.

COVID-19 has had a great impact worldwide. One of its many effects was that offices had to be closed, which negatively affects the economy and people’s mental health. It is important that a safe way to keep offices open is found. Non-Pharmaceutical Interventions (NPIs) are an important way to do this. However, to pick the right NPIs for one’s office, knowledge is needed of the effects of the different NPIs within the given context. As of now, there are not many models that can provide a detailed estimation of COVID-19 spread and quantify the impact of NPIs in office spaces.
Therefore the existing model of the PedestrianDynamics - Virus Spread (PeDViS) of Duives et al. (2022) is chosen as the base model to be extended into a model that allows the comparison of the effects of NPIs in an office space.
The NPIs of which the effects are explored in this research are:
• All employees wear a mask while walking
• Increasing the rate of cleaning
• Increasing the ventilation rate from standard rate to the rate advised by government officials
• Allowing only 50 % of the employees to come into the office
• Allowing a maximum number of people in the meeting rooms, equal to 50 % of the capacity of the meeting rooms

A sensitivity analysis has been performed to learn more about the impact of some of the variables related to the meetings held in the office. Both the size and frequency of meetings were found to have some influence on the number of infections.
Next, policy analysis has been performed to learn more about the effectiveness of the NPIs, both individually and combined into policies. This led to conclusions on how large and consistent the effect of each of the NPIs was in reducing the number of infections in office contexts. The results also provided information on under what circumstances NPI could best be implemented and what NPI policies are recommended to be implemented to adhere to different levels of safety. Also, various limitations of this research were identified and recommendations for future work were given for part of them. The most important recommendations for future work are:
• Extending the model to include shared workspaces and the activity of having lunch.
•Testing the impact of more aspects relating to activity scheduling
•Gather data on people’s behaviour when the number of days they can come to the office is limited, to learn more about the inconsistent effect of this NPI.
•Test the impact of the NPIs in a context in which the initial infection risk is low.

The Dutch Ministry of Infrastructure and Water Management is committed to enhancing the accessibility and sustainability of the Dutch mobility system. Working from home (WFH) has been discussed as a policy lever to decrease commute travel and, thereby, congestion for decades. During the COVID-19 pandemic, this discussion regained momentum. WFH was a key factor for the decrease in commute travel by public transport and car in the Netherlands, which decreased congestion. Against this background, the Ministry wants to stimulate WFH post-pandemic to retain some of the sustainability and accessibility benefits identified during the pandemic. WFH can enhance the accessibility of the mobility system thanks to reduced commute trips during peak hours.
Although the stimulation of WFH may be a policy lever to increase the accessibility of the mobility system, the full effect of WFH on overall personal mobility is unknown. Studying pre-, during and post-COVID-19 measures data, this study assesses the impact of a change in WFH on activity-travel patterns. Commute travel, non-work travel, and mode use together define activity-travel patterns in this research. The main research question is as follows: How did changes in WFH influence activity-travel patterns during the pandemic in the Netherlands? To answer this question, data from the Netherlands Mobility Panel (MPN), collected between 2019-2022 is used. Latent class analysis is used to identify seven distinct activity-travel patterns. Next, two latent transition models are estimated. The first model, for 2019-2021, studies the effect of an increase in WFH on transitions in activity-travel patterns. The second model, for 2021-2022, studies the effect of a decrease in WFH on transitions in activity-travel patterns.

The application of LTA, a longitudinal clustering method, sheds light on complex transition behaviour in the population during- and post-COVID-19 measures. The results show that activity-travel patterns before a change in WFH and the level of an increase in WFH matter for the impact of WFH on transitions. Depending on the initial class membership, differences in transitions emerged. To a certain extent, all effects of WFH on travel, substitution, complementarity, modification, and neutrality exist in the population, but these depend on initial class membership. The results for an increase in WFH are significant, while results for a decrease are not. For the time being, these findings do not exclude the existence of structural changes in working and travel behaviour. Significant effects on transitions appear for a small and a large increase in WFH. After an increase in WFH, most classes are more prone to take up another activity-travel pattern. In both cases, most classes have an increased probability of transitioning towards the low mobility class, which underpins more sustainable patterns. Nonetheless, more diverse results appear for a small increase in WFH, and significant transitions towards other car profiles appear. Analysing the impact of a small increase on class sizes confirms this. Overall, the car class sizes remain relatively high after a small increase in WFH. Thus, an increase by 1-3 days working from home may not lead to more sustainable activity-travel patterns in terms of trip rates per purpose and mode. Nevertheless, it needs to be kept in mind that this study only analysed trip rates per purpose and mode, a small increase in WFH may still lead to a peak-travel reduction, which benefits accessibility. Further analyses should investigate peak-travel to conclude on the accessibility benefits of small increases in WFH. At the same time, governments and employers should take measures to counter-act low mobility associated with WFH which is detrimental for physical health.

The rise in urban stresses has prompted the need for preparedness of urban areas to face precarious circumstances. Consequently, the concept of urban resilience has grown in popularity not only to tackle sudden shocks but also to face long-lasting socio-economic tensions. To implement the arrangements for resilience policy, literature suggests that social factors govern the resilience of urban areas. Communities in which the residents work together and have common goals have a stronger willingness to cooperate. To this end, social cohesion has been proven to be significant for subsistence in the event of a catastrophe. Cohesive communities protect residents against threats, care of others during hardships, and ultimately promote community resilience.


Balanced neighbourhood policies aim to strengthen the cohesion between citizens, communities, and social institutions departing from the assumption that social mix fosters social cohesion. Their goal is to increase the social mix of specific areas to avoid the clustering and segregation of disadvantaged households to, as a result, promote resilient actions. There is, however, literature that suggests that the anticipated effects are rather inconclusive and usually not achieved. Instead, balanced neighbourhood policies would promote segregation by forcing the displacement of groups of residents.


The issue arises whether balanced neighbourhoods trigger resilient actions that are pivotal in resilient communities. In other words, does neighbourhood balance increase resilient action of neighbourhood residents? We took a cross-sectional confirmatory approach to understand the social mechanism that triggers resilient action in balanced neighbourhoods based on Partial Least Squares—Structural Equation Modelling (PLS-SEM) and spatial econometrics. To this end, the research tests the assumptions on which the municipality of Rotterdam grounds their Resilience Strategy's balanced neighbourhood policy, the Woonvisie, using a 2019 public survey on social development. Rotterdam's definition of a balanced neighbourhood is defined from a set of conditions for the amount of houses in different house price segments. Therefore, the tested model is based on the grounds that geographically connected people become affected by their neighbourhood's balance to promote resilient action. As such, we use the willingness to help friends and neighbours to characterise informal support as resilient action. Here we show that balanced neighbourhoods are associated with less informal support: the higher the balance, the fewer residents are willing to help their friends and neighbours.


The results indicate that social cohesion is the social mechanism that triggers help between friends and relatives and fully acts as the mechanism for resilient actions triggered by the balance in a neighbourhood. From the multiple combinations of houses in different house price segments that the definition of balanced neighbourhoods allows, we distinguish two associations. On one hand, house price distributions which foment a reduction in polarisation (more middle-priced houses) are negatively associated with social cohesion. On the other hand, balanced neighbourhoods which foment polarisation (more low- and high-priced houses) are positively associated with social cohesion. This indicates that our results are in line with Putnam’s homophily principle, i.e. ‘birds of a feather flock together’. This outcome is opposite to the policy discourse of governments in favour of balanced neighbourhoods, including the municipality of Rotterdam, that mixed neighbourhoods foster social cohesion and therefore resilient action.


The testing of the theory is complemented in two ways. First, we show that social cohesion and informal support are not constrained by administrative boundaries, so the social perceptions and actions in nearby neighbourhoods affects the level of the other neighbourhoods. Second, we found no moderating effect of factors related to the demographics and the built environment that can promote or deter social interactions, and thus are aspects of what can be considered a resilient neighbourhood.


The analysis also shows that Rotterdam's definition of balance allows multiple and dispersed combinations of the amount of houses in each price segment, which can result in counterintuitive conceptions of balance. In addition, the results show apparently contradicting results of the relationship between balance and social cohesion depending on whether the distribution foments house price polarisation. As a result, we argue that the definition is under-specified and can be misleading.


Finally, only 2.1% of the possible balance distributions yielded an acceptable goodness-of-fit of our model. This could be indicative that the model needs to be reevaluated. We found that neighbourhood ethnic heterogeneity and house type heterogeneity are directly associated to social cohesion and informal support, respectively. Future research should elaborate on the theory on which the model is grounded and create coherence to the empirical relationships identified. In contrast, the few fitting distributions could otherwise indicate that that social cohesion and informal support cannot be explained by the balance in a neighbourhood and that the policy should be reevaluated. Under this second interpretation, the study has uncovered which are the balance distributions for the city that can actually show the alleged effects of balance.


Based on these findings, a policy advice is formulated. If the objective is to increase social cohesion and resilient actions, we discourage the municipality of Rotterdam to approach this by building balanced neighbourhoods. Nevertheless, we have seen that building social cohesion is a way to build social resilience, so recently developed city programs focused on the development of neighbourhood organisations, which not only provide a space for social cohesion but also to collect and share resources directly, are a step forward from the Woonvisie.

The COVID-19 pandemic had a strong impact on travel behaviour and Dutch societal perspectives on that travel behaviour. During 2020 and even the first half of 2021, the number of airplane passengers and train passengers saw a massive drop during the pandemic. The car, while definitely affected early in the pandemic, saw less of a drop and car traffic has almost returned to pre-pandemic levels. This, despite the rise of working from home that took place during the pandemic. As rush hour traffic jams appear to have returned full force and public transport use appears permanently negatively impacted, it becomes important to evaluate how public attitude towards the daily commute and car usage have changed during the pandemic to revaluate public policy goals. This change in societal perception of methods of daily commute, like car and public transport use or working from home, after the pandemic is evaluated using mixed-quantitative methods, including Q-methodology. The research results in different identified societal perspectives on commute, primarily representing changed attitudes of car users and multimodal cyclo-pedestrians, their attitude being structured primarily by their embrace or rejection of working from home. The research adapts attitudinal theory by van Wee, et al. (2019) and gives insight in the efficacy of implementing dynamism into Q-methodology. Future research will look into applying and adapting gained insights into Dutch societal perspectives on future transport policy and possible future dynamic Q-methodology studies regarding the topic of travel and attitude.

Increasing emissions from aviation combined with the intensifying climate change, highlight the need for a sustainable change in the aviation sector. Since innovations making flying in itself more sustainable are taking long to implement, a behavioural change (flying less or not at all) is
necessary.

Recent societal developments show how people’s attitudes to flying might play a role in flying behaviour change, and how attitudes might be changing. Earlier research has established a link between people’s behaviour and attitudes. However, studies addressing changes in air travel attitudes and what causes those changes are not in abundance. Hence, this thesis empirically explores whether and how people
have changed their attitudes towards air travel and to identify what factors contribute to a change in attitude.

This was investigated by applying a dynamic approach to the Q methodology. Three viewpoints on air travel were identified. Consequently, changes in attitudes and contributing factors to attitude change were identified.

This research finds that people in The Netherlands have changed their attitudes in the recent years. The attitude changes
were synthesised into three main directions, namely; people expressing a more environmentally-aware view on air travel, people expressing a more independent view on air travel, and people expressing a less self-evident view on air travel. The factors contributing to the identified changes can be summarised by people knowing more, having different emotions and engaging in certain behaviour.

Finally, this study has made three scientific contributions. The first is an empirical contribution, as it has added to the currently scarce knowledge base on changing air travel attitudes through conducting a first exploratory and empirical study into how attitudes to flying are changing among a group of Dutch people. The second is a theoretical contribution, as this study successfully applied and therewith partly validated the model on attitude change of Van Wee et al. (2019). Third, this study has made a methodological contribution, as it incorporated a dynamic approach in the Q methodology and thereby addressed its suitability for studying attitude change. Regarding the societal relevance, this research resulted in suggestions for policies that aim at creating attitude change on a larger scale in society, which in turn could stimulate the necessary behavioural changes, i.e. people flying less.

Hydrogen is more and more recognized as an essential component for a sustainable future. Because of its versatility, hydrogen is a promising energy carrier for decarbonization of various sectors. However, to realize the promising sustainable characteristics of hydrogen as a carbon-free energy carrier, hydrogen production through zero-carbon means is a prerequisite. The current rate of diffusion of green hydrogen is sluggish, caused by a number of impeding barriers. To enable green hydrogen to play a role in a sustainable future, acceleration of diffusion through the implementation of policy instruments is required. This research applies a System Dynamics modeling approach to simulate the diffusion of green hydrogen over time and assess the impact of policy levers from 2010 to 2050 in Germany. For two scenarios, the impact of a diverse range of individual policies and policy combinations has been examined, with policy settings varying in strength and timing. In the reference scenario without policies, the incumbent situation was corroborated, with green hydrogen diffusion initiating very late with a marginal rate, leaving the green hydrogen targets of the German government out of sight. From the policy exploration, is has become apparent that discounting electricity levies and carbon contracts for difference are the most effective policies for supporting green hydrogen production and hydrogen demand. Nevertheless, the policy analysis showed that the implementation of unconventional policies such as a ban on carbon capture and storage, is required to reach a sufficient rate of diffusion and achieve national targets. It was also demonstrated that the simultaneous implementation of supply- and demand side policies is a primary condition for the diffusion of green hydrogen. Finally, it was found that the timing of policies might have a counter-intuitive influence on the policy effectiveness. These results confirm the urgency and necessity of policy implementation to stimulate green hydrogen diffusion and provide preliminary insights in the complex dynamics of diffusion. From the modeling outcomes, several policy recommendations about the type, timing, and strength of policy measures have been formulated.

One of the most common hurdles especially in large infrastructure projects is scope change. Scope changes negatively affect time, quality and usually lead to cost overruns due to underestimating the change order impacts on the project. There are two main categories through which the impacts could be quantified: the first and second order effects. The first order effect is the impact of the change, cost, time, quality, and risk related. The second order effects are the impacts that resulted as consequences of the first order effects such as lower labour productivity and increase in errors.

The purpose of this research was to analyse and quantify the second order effect due to scope change in large infrastructure projects. The approach that was used to create the model is system dynamics modelling (SDM), since several studies confirmed the success of system dynamics modelling in solving similar projects’ problems in different industries.

Through the literature review and the case study, it was found that the second order effects of scope change are rework, schedule pressure, morale, overtime, productivity, hiring new staff, and office congestion. It was observed through the created dynamic hypothesis that productivity was directly impacted by morale, office congestion, schedule pressure and overtime. The simulated model showed that productivity and morale were the two mostly influenced factors by the scope change.

The main question of "How could second order effects of the project scope change be quantified through system dynamics modelling?" was answered as follows: First, a dynamic hypothesis should be created and confirmed through literature and continuous interviews till the hypothesis reflects the project case. Second, formulas should be created for the defined variables and values should be inserted in the model based on the case data. Third, the model should be simulated, and the perceived progress should be compared with planned progress. Then, the first order progress should be compared with the planned progress to quantify the first order of effects of scope change. Finally, to quantify the influence of the second order effect of scope change, the first order effects should be deducted from the total influence of scope change.

The Netherlands suffers from a traffic congestion problem, which is a cause of both economic damages and high NOx and CO2 emissions. Today, the bus accounts for only a small percentage of all trips made in the Netherlands (less than 4%) but the bus plays an important role as an access and egress mode for train travelers. Short access and egress times are considered as crucial criteria for choosing to travel by train. Thus without a proper bus network, traveling by train will become less attractive. Furthermore the Dutch population is both growing and more mobile than ever, but space to construct new roads is limited. These developments mean that the current transport system in the Netherlands will not be sustainable in the future. To prepare it for the future sustainable public transport will be essential. However it is complicated to design a solid bus network as the public transport sector depends heavily on subsidies. For bus operators to allocate their resources efficiently, good information is key. Direct Ridership Models (DRMs) are rising in popularity as they can provide useful information to bus operators. DRMs are statistical models that are able to capture the relationship between surroundings (such as car ownership in the surrounding neighbourhood or the presence of a university) and travel demand. This way first cut predictions can be made for new bus stop locations. Furthermore, the expected number of travelers can be predicted for existing bus stops when there is a change in the surroundings of the bus stop, which allows bus operators to adjust their services accordingly. In this study, DRMs are used to explain and predict bus ridership for the concession area Arnhem-Nijmegen. The independent variables that are included in the models can be categorised into three groups: demographics, built environment and level of service. Three DRMs are developed based on different regression models. One model is the commonly used OLS. The other two models, Spatial Lag X model (SLX) and Spatial Error Model (SEM), are able to take spatial relationships between bus stops into account. The spatial models showed an improvement over the OLS model in terms of explanatory power. With 34 independent variables, the OLS model could explain 71.4% of the bus ridership. The SEM was able to explain 72.4% and the SLX 73.4%. But more importantly, some of the independent variables that were found significant related to ridership in the OLS model turned out to be not significant in the SEM model, of which car ownership was the most surprising one. For Arnhem-Nijmegen, it could be further looked into if the transportation by bus to parks, sport facilities, amusement parks and hospitals could be facilitated more to increase the bus usage as these variables are found to influence bus ridership significantly. Furthermore, it should be examined why P+R's are not found to affect bus ridership significantly. The prediction accuracy of the OLS model and the SLX model was also studied. The predictions of the SLX model turned out to be more accurate, but in general the prediction results were not satisfying. For more than half of the bus stops in Arnhem-Nijmegen, the predicted value of the SLX model was 50% more or 50% less than the actual value. The reason for this is the existence of non-stationary relationships, which means that the relations between ridership and the independent variables can vary based on the locations of the stops. This study contained bus stops in urban areas and in rural areas and it can be expected that for example the influence of a hospital in a village on bus ridership is smaller than the influence of a hospital in a city. In this study, the existence of non-stationary relationships between regions is demonstrated while studying if a DRM based on Arnhem-Nijmegen can be generalised to the region Groningen- Drenthe. It turned out residential land-use has more influence on bus ridership in Groningen- Drenthe while hospitals and universities have a higher impact on ridership in Arnhem-Nijmegen. The non-stationary relationships make it not advisable to generalise the DRM based on Arnhem- Nijmegen to different regions. In scientific literature, nothing has been written about the practical applications of DRMs for policy makers. As it was assumed DRMs can be useful for policy makers, three policy makers were interviewed for their insights on the applications. One of the application mentioned is preventing unsafe traffic situations, as DRMs allow to update ridership predictions when circumstances around the bus stop change. For example, when a bus stops is designed for a low number of travelers and a new neighbourhood is developed close to it, the increase in travelers could result in unsafe situations. With a DRM the increase in travelers can be predicted, which makes it possible to redesign the bus stop. Other applications that were identified in the interviews are: examining if a bus stop is performing below its potential and substantiating plans for new routes, new stop locations and relocating bus stops. Overall, the benefit of DRM is widely acknowledged and in combination with personal expertise, it could lead to optimised decision making. In the end, it is demonstrated how a DRM can be used to identify bus stops which do not fulfil their potential and suggestions are provided how the number of rides can be increased. The stops Europalaan in Renkum, Kerkstraat in Weurt and Beemdstraat, Fransestraat & Hatertseweg in Nijmegen turned out be under performing and several improvement suggestions were made. Although the prediction accuracy of the models were rather poor, future studies can use this study as a basis for models based on a more homogeneous study area, such as only bus stops in cities. Sub-setting the model is expected to have a major positive influence on the prediction accuracy. It would be interesting to see if a DRM based on a city can be generalised to other cities as well. Furthermore, the model can be complemented with more independent variables. Variables that encourage car use over bus use, such as the difference in travel time to the city center or the distance to the closest highway ramp, could be valuable additions.

In recent years, living needs of residents have been greatly enriched. The development of urban logistics industry is increasingly important for the overall development of the city. At the same time, in the context of China's remarkable improvement in urbanization and the rapid increase in urban population, the development of urban logistics is also facing many problems. First, high-density logistics demand in cities has increased the difficulty of organization and management. Second, urban logistics has to face urban land use planning and limited resource allocation. Third, urban logistics will have an external uneconomic impact on the urban environment, transportation as well as economy activities. The sustainable development of urban logistics and the interaction between logistics activities and other aspects have become an urgent research subject. The urban logistics system is taken as the research object in this thesis. In order to solve the problems existing in the modern urban logistics system in China, scientific planning methodology and feasible strategies are proposed in this article, aiming at improving the development of the logistics industry and promoting the sustainable development of cities in China. In order to be more realistic, a case study is employed. Chongqing, the representative city of the central and western regions of China, is selected as the study case object. Unlike other old-fashioned first-tier cities in China, Chongqing has experienced the process of rapid industrialization. The analysis of Chongqing has a good reference for many of China's quasi-first and second-and-third-tier cities. Through the analysis of the background of urban logistics development and literature research, the core research question of this thesis is put forward. “How can we propose a reasonable development strategy to promote the sustainable development of modern logistics in Chongqing while ensuring the optimal results of the entire system?” In order to explore this problem, this paper begins with an analysis of the urban logistics system architecture, the role of public policy, the evaluation standard for the development of urban logistics system, and conducts an in-depth study on the status quo and challenges of the logistics industry in Chongqing. The analysis results show that Chongqing logistics faces four major difficulties. Firstly, the global trade volume and logistics demand is shrinking. Secondly, logistics corporations is scattered and lack of modern technology and services. Thirdly, there is a greate shortage of senior logistics talent. Fourthly, the traffic congestion in the main urban area is severe. Based on the theoretical analysis and research, a system dynamics model is constructed. The model highly simulates the internal supply and demand structure and historical data of the logistics system in Chongqing. Through the analysis of the model operation, the comprehensive effect of several strategies on various evaluation criteria are simulated and analyzed in this paper, including promoting the growth of trade demand, improving modern service capability, attracting logistics talents and alleviating traffic congestion. Based on the simulation results, the optimal policy suggestions for the sustainable development of urban logistics in Chongqing are put forward.

The Dutch heating transition involves changing the heating systems of eight million buildings to a sustainable alternative by 2050. Many heating system technologies are available, but deciding which systems are cheapest for all these buildings is a difficult question to answer. Local policymakers are increasingly making use of heating transition models that estimate the feasibility and costs of systems in municipal neighbourhoods. The applicability of these models is limited by the degree of uncertainty about the future as well as the complexity in communicating the model results to policymakers. Sensitivity Analysis (SA) is a tool with which the most influential model uncertainties can be identified, quantified and communicated. So far, limited energy transition model studies have extensively used this method. A case study of SA on the CEGOIA heating transition model was performed to fill this gap and evaluate SA’s value. CEGOIA calculates the costs of a variety of heating systems and optimizes the allocation of scarce energy carriers such as green gas and hydrogen to find the lowest societal costs. Sensitivities of eight heating system options were analysed in different archetypical neighbourhood contexts using Fractional Factorial analysis, the Method of Morris and the Sobol’ Method. Out of an initial set of 953 parameters, a subset of less than a dozen highly influential variables – consistent between neighbourhoods of different physical characteristics – was identified for each heating system option. High sensitivities indicate that changing the value of a parameter leads to a large change in total costs. These sets, therefore, describe exactly what uncertainties are crucial to evaluating what heating system is the cheapest possible solution. Variables in these sets include, but are not limited to, the price and infrastructure costs of electricity and gas, heating installation costs and insulation costs. Interviews with other heating transition model owners further illustrated that the use of systematic SA as done in this analysis is not the norm. Besides results and insights from the CEGOIA SA, further applications for SA in heating transition modelling is postulated to be able to improve the modelling process, as well as better, understand complex model dynamics. One recommendation is, therefore, to include SA as part of the toolkit for the large heating transition models currently being used in the Netherlands. The main barrier for doing so with CEGOIA is the computational time of the model, which limited the number of parameters that could be evaluated as well as the SA techniques that could be used. Still, a more systematic analysis of sensitivities in heating transition models will provide insights that ultimately aid Dutch policymakers in making robust decisions.

The urban mobility sector is developing at a fast pace and is demanding transportation systems to be resilient and sustainable. Policy makers deal with an extensive offer of new technologies but lack an overview of the contribution of these mobility solutions to their urban planning ambitions. Uncertainty exists regarding both the technological advancements and the future of the mobility sector in a more general sense. Hence, there still seems to be a challenge to overcome for policy makers to transform the mobility system and adapt to the developments. The applications are clear and literature on implementation is present, but no specific guidance in terms of trade-offs is available to governmental organizations working on urban infrastructure. This is also shown by a lack of literature on decision support systems for smart mobility. On top of that, there is a more practical motivation for a tool that provides an overview on the possibilities in smart mobility applications and their effects. Engineering and consultancy firm Witteveen+Bos requested a design for such a tool, to be able to provide more insightful advice on smart mobility solutions to their clients.
The present study concerns the development of a tool that gives insight in the (expected) effects of investments in different smart mobility solutions The research question is formulated as: What is a useful tool for policymakers to quickly assess effectiveness and efficiency of smart mobility applications?
The tool designed assesses the mobility situation in a municipality, suggests smart mobility applications that are relevant in regards to specific mobility ambitions, and has set the first steps for a framework to assess the effects of smart mobility applications. On top of that, the tool is verified by testing for all user and system requirements that were set by consulting policy makers and experts, evaluated by means of a walk-through of all the units in the tool, and validated with the envisioned users to evaluate if the tool is relevant and supports their decision-making process.

Waste-to-Energy (WtE) serves as an important source to meet the growing energy demands of the world in a sustainable manner. It can help in the reduction of Greenhouse Gases (GHG) emissions, lower the risk of environmental pollution and contribute towards the establishment of a circular economy. By generating steam or electrical energy, WtE helps avoid the CO₂ emissions from fossil-fuel based electrical generation.  But it requires further improvements within the area of waste management. One of the key objectives of waste management is to improve the source separation of Organic Fraction of Municipal Solid Waste (OFMSW) generated by households and Small-Medium Enterprises (SMEs). The increase source separation of waste, positively impacts the WtE process. To achieve high source separation, the presence of pro-environmental behaviour and the appropriate waste collection infrastructure is important. In this thesis, the impact of these factors on the waste separation rate and the energy generated from OFMSW is evaluated by constructing an Agent-Based Model.  The results of this thesis show that combined influence of Pro-environmental behaviour, higher ratio of households to SMEs, large bin size and choice of an efficient WtE technology to treat source separated waste, resulted in an 16.24 % increase in the energy generated from OFMSW and a 25.32% increase in the CO₂ abated.

On the 22nd of April 2016, now also known as the ”Earth day”, the Paris Agreement has been brought to live. This agreement is constructed by all nations to fight together against a common enemy, namely climate change. Over the past few years it has become more difficult then ever before to ignore the effects of global warming. While species which have lived on the face of earth for over a thousand years are dying out, while natural disasters such as typhoons, tsunamis, extreme droughts and floods are drastically increasing in occurrence and while we measure new record years of climate change perceiving every year over the past decade it finally becomes inevitable to start taking measures to fight against climate change in order to allow humans and all other species living on this planet to survive. The Paris Agreement is one of the leading most respected agreement combating this cause, it bring together all the nations to sum up their efforts in order to try and mitigate and adapt the effects of climate change. The main goal of this act is to enforce the global response to the danger coming from the change in climate. To achieve this goal a target has been placed which states, the global temperature rise this century should stay well below 2 degrees Celsius. Every country has made its own individual pledge to help achieve the goal set by the Paris Agreement. Indonesia the 4th most populated country in the world is one of them. Indonesia is not only one of the most populous countries, but also one of the most polluting countries, with emissions coming from deforestation and enormous coal-fired power generation pipelines. Next to this, Indonesia’s location makes it highly vulnerable to the consequences of climate change. Therefore, Indonesia pledged to reduce it’s greenhouse gases, however up until now their targets have been rated as “Highly Insufficient”, meaning that they only show modest signs of improvement. The cause of this, is their development of coal fired plants, as they plan to build further 6 GW of such power plants by 2022 and around 27 GW by 2028. Therefore, it becomes inevitable to star moving the investments in coal for the next years towards renewable energy sources, such zero-carbon methods are for Indonesia to start the right pathway to attain the Paris Agreement and sustainable development. This is the reasoning behind the development of this research. However, since entire Indonesia seems like a too big of a challenge for one research. Therefore, this research aims at picking one of it’s islands and hoping that by designing an effective road map for the energy transition on this island will lead for others to follow. The island of choice has been Bali, a well knows and highly visited island in the archipelago. The target set by Indonesia are taken over for the case of the energy sector in Bali. These targets are the following, first a foremost a total greenhouse gas reduction of 11% by the energy sector compared to the reference scenario should be achieved by 2030. Second, to help meeting the first target a 23% penetration of renewable energy in the total electricity mix should be achieved by 2025. Last, these two targets should not hinder the expansion of the total capacity to satisfy the growing electricity demand at all time. From these targets and a literature review about energy transition on islands and renewable energy technology on islands the following main research question as been constructed: What plausible road map can ESDM follow, in order to accelerate the energy transition in Bali, while assuring that the demand is covered, to help Indonesia achieve their targets? In order to find the solution to this question four sub research question have been set up. These require the implementation of a system dynamics model. From the literature study and research done on similar islands which have already achieved a successful transition towards renewable energy, a total of 15 different scenarios have been constructed. These scenarios are possible outcomes of particular applications of 8 different policies, which the Ministry of Energy and Mineral Resources (MERM) or in Indonesian Energi dan Sumber Daya Minera (ESDM) has the power off to apply. First, a system analysis is given, which introduces all of the information required of the system, which is in this case the island of Bali and its electricity network. This analysis also demonstrates the current structure of the electricity network and explains why the transition of renewable energy is happening at such a slow pace. There are multiple reasons for this, ranging from cultural barriers to overcome as also geographical difficulties for the construction of renewable energy sources. In addition, there is also the issue that the balinese people are used to coal and oil powered electricity and see renewable technologies as methods coming along with a high risk. After the system analysis setting the boundaries of the complex system a road map development is introduced. In this chapter, it is explained how the different policies have to be applied, in order to construct the 15 different simulations of the future. This is followed by a chapter describing the model setup. First a conceptual model is designed including all of the mechanisms in work at the electricity network transition. The implementation of these feedback loops and of the policies into the system dynamics model is explained in great detail. Next to this, a table can be found in this chapter including all of the main input variables with the sources and assumption taken to determine their values. Before running the simulation with the final input variables, a verification and validation of the system dynamics model is demonstrated. This chapter verifies that the computerised model represents the conceptual model introduced before. Next, a validation testing if the simulation can support what it stands for is carried out. This has been done by investigating whether the main theories and assumptions elemental for the conceptual model are valid. In addition to this, the model is checked on errors and whether all of the units included in all of the equations are valid. At the end of this chapter a sensitivity analysis is executed. In this sensitivity analysis the different input variables are modified, in order to see the response on the output variables. This ensures that the system dynamics model representing the electricity network of Bali is indeed valid and can be use for further analysis. In the follow up chapter, the results of the different simulations are given and they are being compared to the main targets introduced at the beginning. From the total of 15 scenarios there are only 5 which meet the most important requirements. Therefore only these 5 scenarios are deployed into a trade-off table. The conclusion of this chapter is that the scenario S12+TE is the best choice for ESDM. This scenario represents a 24% growth rate of the solar PV sector stimulated by ESDM. Next to this, it requires a modification of the onsite oil fired power plants, so that they can run on cleaner fuel, namely liquefied natural gas. In addition to this, this scenario also requires to raise the taxes on fossil fuels, give out subsidies for solar PV projects, fix the land cost of farmland which has potential for solar farms and it includes an evolution of efficiency of photo voltaic cells from 17% to 30% within the next 10 years. This scenario would have a total cost of 2,050 million US dollars, to ensure them that they would achieve all of their targets by 2030. This result is then being reflected on in the final chapter, where some concluding remarks are given on the different results and methods. Next to this, several ideas are given to help improve any future follow up work on this research.

Reducing GHG emissions has become a widely publicized topic to halt future effects of global warming. In an effort to accelerate the energy transition a group of policy-maker from McKinsey developed a tool named marginal abatement cost curve (MACC) capable of illustrating the relationship between the cost-effectiveness of different abatement options and the total amount of GHG abated (Bockel et al, 2011). Even though the MACC has become popular for government reports and environmental analysis of abatement options through their abatement cost it lacks to analyze the options beyond the financial perspective. In 2016, Chappin published the introduction of the Y-factor method with the aim of solving the why factors that were hampering the pursual of implementing the abatement options. The method relies on the use of grading each abatement option in a scale of 0 to 2 through 12 socio-technical factors that are divided into four categories: multi-actor complexities, physical embeddedness, behavior and the cost & financing. This new method is a more robust approach than the MACC and it helps in providing new insights across different categories. The Y-factor is a relatively new method that has been furtherly assessed by Arensman (2018), Cheung (2018), and Soana (2018). This master thesis follows in the method of the Y-factor and goes a step beyond in proving its reliability when applied to a case-study, in this case applying the Y-factor for Mexico. The main research question is What emission abatement curve can capture the complexity of reducing GHG emissions in Mexico? For the construction of the emission abatement curve this research focuses on 20 abatement options that are relevant for the country. These options were selected through a process to provide diversity in the sector and reflect the reality of the biggest GHG emissions contributors of the country (energy creation, transport sector). Through a preliminary scoring based on literature review including government reports, scientific and news articles a preliminary Y-curve was constructed. The validation of the emission abatement curve was provided by contacting different experts in the country. This validation relied on interviews made to provide insights of the current situation of Mexico further understand what is hampering the implementation of the abatement options. To remove subjectivity for the validation each of the abatement option was graded by 2 or 3 experts’ interviews reducing personal bias and increasing result accuracy. The validated Y-curve results had interesting insights when comparing to the initial MACC developed by the US government for the low emission development program in Mexico and presented by Rebolledo et al (2016). The energy sector had the highest scoring abatement options on average which included renewable options such as Geothermal, Wind-Energy, Small Hydroelectric while also including fossil-based options that have become a priority for the new government administration Coal CCS for new plants. From all the options the highest ranked option according to the Y-score was the Coal CCS retrofit with a score of 22 out of 24. This means that this is the least convenient 6 option to be pursued according to the grading across the four categories. It was also interesting to note how the transport sector with options such as modal shift freight transport, transport policy changes, hybrid and electric vehicles, among others conform a highly diverse group each with different goals and widely different factors that obstruct its pursual. A key area is how the transport sector is dependent on a lot of different actors for any structural change or policy implementation meaning that these options are some of the most difficult to follow specially in the cities. Results of the Forestry & Agriculture cluster can be misleading if only the McKinsey data is available given the peculiarities of how the plot areas of land are owned in the country making it a unique situation that is hard to conceive in different countries. A general link between Mexico’s current affairs and the abatement options selected are explained to provide valuable information on the country and possible pitfalls when dealing with similarities in other countries. At the end of the report a recapitulation of the process and the main steps of the thesis are provided, as well as, concluding remarks for each chapter are mentioned to highlight the most important aspects of them. Valuable suggestions given by the interviewees on how to improve the Y-factor method have been highlighted, as well as the limitations of the study and how it can be improved. The societal and academic relevance of the project, as well as the limitations of the study are addressed and given an opinion of the added value of using the Y-factor for future research purposes is given. Concluding, the Y-factor approach adds value to decision makers and serves its main purpose of understanding the factors that hamper the abatement option implementation while also helps in unravel the complexity associated with such abatement options to a better overall understanding.

Disinformation is a problem that has become more relevant over the past years. Also the European Union tried and is trying to deal with this problem by introducing multiple policy actions. However, the impact and usefulness of these policies is not clear. This thesis uses a combination of stakeholder analysis, interviews and systems thinking to assess the impact of current policies the reduce the spread and production of disinformation. This assessment serves to further improve current and future policy actions. Results from the analysis indicate that current policy actions are not focused on the root causes of the production and spread, and can be improved, among other things, by introducing regulation, making policies more goal-oriented and being more clear about policies in terms of content.

Greece was amongst the first countries to be hit after the outburst of the European financial crisis in 2008. The crisis revealed the underlying intra-Eurozone macroeconomic imbalances and turned the attention of the global community and investors towards the very high level of Greek public debt. Private investments in the Greek economy also suffered a heavy downfall since the beginning of the crisis. The level of private gross capital formation kept decreasing since 2008 and forecasts estimate an almost null growth for 2020. This research tries to answer the question of what the main factors behind the decline of private investments are and which policies can re-activate them. To investigate further and answer the question about the link of public and private investments a System Dynamics model of the Greek economy is built based on a traditional macroeconomic approach. With the use of the System Dynamics model for the Greek economy. Next, the Exploratory Modelling & Analysis (EMA) Workbench tool is used to perform the analysis. Two different policies are introduced. The first one concerns a mix of lower taxes and lower government investments and the second one is based on a policy of higher taxes and higher government investment. Sensitivity analysis with the use of SOBOL technique and scenario discovery analysis with the use of PRIM are used to explore the most sensitive factors with regard to private investment.

In order to determine how the energy sector of the Netherlands can or should look in the future, energy scenarios are used to explore possible alternative futures. Many scenarios for the Netherlands exist, but a classification of contemporary Dutch electricity scenarios is missing. Additionally, agent-based modelling (ABM) has often been proposed for energy scenario development as it is able to model the uncertain behaviour of energy companies, which is not possible in more traditional energy scenario models. However, a recent publication of the impacts of the Dutch climate agreement seems to suggest that agent-based modelling is not yet used to develop energy scenarios in the Netherlands. This research first classified Dutch energy scenarios for 2030 which are focused on the electricity sector. Additionally, it analysed two Dutch energy scenario with EMLab, an agent-based model. The agent-based model was able to replicate the scenario results when the inputs of that scenario were used. Additionally, it showed how uncertain behaviour would cause overinvestments and what measures can be used to impact this behaviour. The results show that ABM can and should be used to support scenario development for the Netherlands.

In this thesis, the System Dynamics (SD) model of the Full Reserve Banking (FRB) system built by Van Egmond and De Vries (2016) has been reviewed and improved. The improved model is used to see the effect of FRB system in preventing a long-lasting recession as one occurred in 2008. FRB system is a monetary reform which attempts to separate private banks’ monetary and credit functions. Under FRB system, central bank creates money depending on the price level. Therefore, theoretically, FRB system can prevent recessions by maintaining the price level constant. Van Egmond and De Vries have modeled FRB system using System Dynamics (SD) modeling technique. However, their model has a few model problems which reduce reliability of the model; Ⅰ) the model crashes in different conditions, Ⅱ) the model is based on some assumptions that are less realistic or logical Ⅲ) the measurement units of the model are not specified. In this thesis the solutions for these model problems were sought through a model review and desk research. In the original model, high house prices and debt level are a direct and definite reason why the recession begins. Households default because house prices went up too fast and they could not afford the debt repayment. However, in the improved model, households default because their wages are stagnated. Households default when wages are stagnated while debt level grows high. In both original model and improved model, FRB system successfully protects the price and wage level. Under FRB system, the probability of recession decreases significantly. However FRB system is found to be not effective in stabilizing the price level when the firms’ dividend payout ratio is high. Furthermore, FRB system was not necessary as households do not default when the firms’ dividend payout ratio is low. The reason that FRB system’s effectiveness is decided by dividend payout ratio is because the model was calibrated originally with dividend payout ratio of 100% which is unrealistic in the real world. Therefore, the model is expected to be calibrated properly especially focusing on dividend payout ratio in a future work. Furthermore, it is recommended to include trades with other countries in the model in further work.