The need for water, energy, and food resources is central to human being. Demand of these resources will increase as global population increases. By 2050 it is expected that combination of population growth and economic development will lead to 50% increase of global water and food demand, while energy demand will be doubled. Addressing sustainable water, energy, and food consumption has also been emphasized in United Nations’ Sustainable Development Goals; among others, this applies to consumption at household level. Sustainable household consumption of water, energy, and food here implies not only the objective to improve the resource durability, but also to reduce environmental footprint. The unsustainable management of these resources will potentially harm the climate objective addressed in the Paris Agreement in which The Netherlands, among other countries, have been participating. Acknowledging the importance of household consumption on the environment, a joint research team funded by the National Science Foundation named INFEWS (Innovations at the Nexus of Food, Energy, and Water Systems) is commissioning this research project to investigate policy measures to sustainably reduce greenhouse gas emission concerning Dutch household food, energy, and water consumption sector. The main objective of this research is to obtain insight on the dynamics and interrelations of Dutch household water, energy, and food consumption; and to develop policy recommendations based on the insight toward reducing the direct and indirect greenhouse gas emissions (GHG) level associated with the household consumption over time in the Netherlands. To achieve this objective, this research mainly utilizes data-driven exploratory modeling approach in which the interrelations of water, energy, and food consumption system are modeled and simulated over time, while acknowledging the plausible parametric and structural uncertainties natural to the state of modeled subsystems. System Dynamics methodology is used to conceptualize the causal relations among the relevant subsystems and to computationally develop the model in a form of stock-flow diagram. Based on the developed model, the Exploratory Modeling and Analysis methodology is used to explore the model within the associated uncertainties. Through these methods, two types of policy frameworks can be respectively tested and explored. The first one consists of the policy approaches within household consumption domain. The second one consists of the plausible policy options outside of the household consumption domain which are relevant to improve the GHG indicator.

At the UN conference of 2015, the promotion of inland waterway transport (IWT) was named as a Sustainable Development Goal by the United Nations. While IWT has been identified to make goods transport more CO2- efficient, the long-term viability of the Dutch IWT sector is at risk. Currently, Dutch IWT businesses benefit from increased demand for transport capacity during climate change induced periods of low river depth by being able to charge higher prices to shippers, thereby increasing short term profits. This thesis research project concludes that this behaviour is unsustainable due to the disregard for providing price competitive and reliable services to shippers, which will ultimately damage the reputation of the Dutch IWT sector. This can lead to shippers choosing other modes of transport, which will damage the long-term viability of IWT demand. A lack of evident optimal climate adaptation measures that would better guarantee the long-term viability of an IWT business under any climate scenario is one of the reasons why IWT businesses have thus far been hesitant towards implementing climate adaptation measures. This thesis research project studies the potential of using a robust decision analysis in supporting IWT businesses’ decision-making on climate adaptation measures that could safeguard the long- term viability of the Dutch IWT sector. It conducts a robust decision analysis and then evaluates its potential using feedback from stakeholders of the Dutch IWT sector. This thesis research project present the results from the robust decision analysis to IWT stakeholders to evaluate how these could impact their decision-making. Stakeholders noted that they believe that the produced influence diagram could be used as a standard communication object to produce a better consensus of how measures influence the long-term viability of IWT demand. Furthermore, stakeholders expressed that the robust decision analysis provided them a better understanding of which measures were able to better guarantee the long-term viability of IWT demand. Results from evaluation interviews with stakeholders therefore provide grounds to believe that robust decision analyses have potential to support IWT businesses’ decision- making on climate adaptation measures that could safeguard the long-term viability of the Dutch IWT sector.

The United Nations' Sustainable Development Goals (SDG) form potentially a great communications framework for financial institutions to report on their indirect environmental sustainability performance. To date, however, no serious effort has been made to specifically assess a financial institution's indirect impact - its impact through the balance-sheets - in terms of the SDGs. Therefore, this thesis sets out to address the following objective: To introduce a prototype system, also referred to as a tool, which is capable of incorporating components of the SDGs so that they may form the criteria based on which financial institutions’ core business activity, their balance-sheets, can be assessed in terms of environmental sustainability. The result of this thesis is a prototype tool programmed in Microsoft's VBA language, aimed to fulfill this objective.

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.

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.

Despite the fact that everyone has the right to adequate housing, this fundamental human right has become a critical manner in several European Union (EU) nations, especially in the Netherlands (OHCHR, 2009; Rosenfeld, 2015; European Parliament, 2020). Despite being the largest social sector in Europe, accounting for approximately 30% of the Dutch housing market, recent research suggests that housing associations are unable to properly handle their social responsibilities in the current environment in the Netherlands (Penders, 2020). First of all, recent changes in the housing market demonstrate that the demand for social housing has not only expanded but also diversified its target audience (Madsen & Ghekière, 2021). Recent studies indicate that aside from the poor and vulnerable, also the elderly, young adults (starters), and middle-income families, along with vulnerable and special groups are becoming increasingly interested in this housing aid (OECD, 2020; Madsen & Ghekière, 2021). In order to meet the rising social housing demand, housing associations are expected to construct more than 25,000 new social houses each year until 2035, with the number of dwellings that must become more sustainable gradually increasing from 25,000 to over 60,000 (Penders, 2020; Madsen & Ghekière, 2021). According to Penders (2020), approximately €116 billion is required for all investments up to and including 2035, however, around €30 billion in social tasks (nearly 25% of the total amount) will not be accomplished since housing associations’ expenditures (interest, taxes, maintenance, and management) are rising faster than their income, namely rentals. In the Netherlands, the creation and management of social housing is the responsibility of social housing associations. However, the different actions that these housing associations can take in order to fulfil their social obligations depend mainly on the housing policies that the Dutch policymakers implement. As a result, it has recently come to light that a significant portion of the social housing stock in the Netherlands is unfit for habitation. One in five of all social rental dwellings in the Netherlands, which is estimated to be home to 312,500 households, are expected to be energy-poor, according to recent studies (P. Mulder et al., 2021; Aw, 2021). In addition, according to current Autoriteit woningcorporaties (Aw) data, the housing associations themselves provided about 80,000 social rental houses in (very) poor conditions (De Regt & Bunskoek, 2021). Given that the Dutch social housing is currently struggling to meet its social obligations due to a paradox of rising demand and constrained financial means. More legislative actions will be required to alleviate the current housing crisis through the social housing market. Therefore, this study aims to answer the following research question: What policy strategy could Dutch policymakers put in place to assist social housing providers to ensure that there are enough adequate rental properties available to meet the rising housing demand? In this study, this research question will be answered by means of a simulation modelling approach. It was decided to investigate the underlying mechanisms in the social sector in the Haaglanden urban region by conducting various analyses (including uncertainty analysis and policy analysis) using the system dynamics (SD) simulation model, the Dutch Social Housing Model, which was created during the execution of this study. Contrary to the conventional SD approach, this study is accomplished by combining the SD approach with the Exploratory Modeling and Analysis (EMA) methodology – which employs computer experiments to support decision-making under uncertainties. Given the dynamic complexity and deep uncertainty linked to the researched system, this method was performed to draw valid conclusions regarding the Dutch social housing market under deep uncertainty (Adams, 2011; Le Roux et al., 2011; Jonsson et al., 2021; Kwakkel & Pruyt, 2015). First of all, the results show that up to and including 2050 there will be a housing shortage in both the social housing market (hereinafter referred to as SGEI) and the housing market for middle-low-income households (hereinafter referred to as Non SGEI) in the Haaglanden urban region. In particular, the shortage of SGEI homes will remain at around 60,000 homes from now until 2050, while the shortage of Non SGEI homes will continue to rise to about 20,000 homes. This can be explained by the difficult financial situation of the social housing associations, which cannot meet their social obligations. Despite the anticipated gradual increase in the social housing associations’ financial sources, this study shows that these corporations will only have enough financial resources to invest in the social housing market. Due to this, it is expected that the housing quality of SGEI houses will improve, while that of the Non SGEI houses is expected to deteriorate. The reason being, that if the housing association has sufficient financial resources for a particular house (in this case the SGEI houses), it can ensure that the homes in question can undergo maintenance, which improves the housing quality. Given that the main purpose of this study is to identify policies that will enhance the system behaviour of the social housing market, it was decided to examine the following social housing regulations: Increase Average Rent, More Subsidy for Social Housing, More Planning Capacity for Social Housing, Lower Interest Rate, and Eliminate Landlord Levy. This is due to the fact that the policies Increase Average Rent, More Subsidy for Social Housing, and Eliminate Landlord Levy all improve the cash flow of social housing associations, with Eliminate Landlord Levy lowering the expenditures while the other two policies increase the income of the social housing associations. The expectation is that improved income flow will put social housing businesses in a stronger financial position, allowing them to make more investments to address the housing shortage and quality. Furthermore, More Planning Capacity for Social Housing guarantees more land at the disposal of housing associations compared to the current situation for the construction of new social housing to ensure that the growing housing demand will be met by the housing supply. It is advised to use a combination of these policies to address the issues with the social housing system because no single of these policies can effectively address both the housing scarcity and the poor housing quality of both housing markets. Based on the results, it can be concluded that the combination of the following policies should be used in Dutch social housing: (1) More Planning Capacity for Social Housing, (2) More Subsidy for Social Housing, and (3) Eliminate Landlord Levy. However, it is advised to ascertain the value of the most influential uncertain parameters prior to the selection of the policy strategy because the majority of the KPIs, with the exception of the housing shortage for middle-low income households, are behaviorally sensitive to the uncertain parameters investigated in this study. Moreover, it is challenging to assess the robustness of the investigated policies because of the significant overlap between the findings of the several policies reviewed by the robustness study. The efficiency and robustness of the various policies mentioned in this study and their combination should therefore be thoroughly examined using the data that have been gathered of the most influential uncertain parameters. This information allows for the most precise mapping of the researched system’s behaviour during the application of the policy interventions. Policymakers are then able to decide on a course of action that is supported by empirical facts as a result. Ultimately, it is recommended that the other actors also be included in the policymaker’s selection procedure, as they may have differing opinions about the different policy candidates. The many perspectives that the actors involved have regarding the social housing system must therefore be thoroughly researched.

Although the motivational capacity of serious games is well supported in theory, few experimental studies have been conducted so far. This study contributes to this knowledge gap by using a serious game to try and increase the currently low student retention in Massive Open Online Courses (MOOCs). For this, a serious game was specifically designed to influence factors that have shown to influence student retention in other MOOCs. A randomized post-test only control group experimental design was used, in which the impact of the serious game on student retention was evaluated with quantitative and qualitative data from questionnaires and the edX MOOC platform. While it was found that the serious game had a negative impact on the intrinsic motivation of students, it appeared that intrinsic motivation did not influence student retention in this MOOC, therewith explaining why no difference in student retention was observed between the control group and game group. This shows that in order to improve student retention in MOOCs with serious games, further research on factors affecting student retention in MOOCs is required first.

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.

Following the Paris Agreement, the national government of the Netherlands reached the Climate Agreement together with more than 100 organizations. As part of this agreement, the industry sector is faced with the ambitious goal of 59% CO2 reduction in 2030 with respect to 1990 levels. Since the naphtha cracking industry is one of the most emission-intensive industries worldwide, decarbonization of this industry can deliver a significant contribution to reaching the climate goals. Among other transformations, electrification is regarded as an important decarbonization strategy. However, electrification in the naphtha cracking industry currently faces several barriers. For an important part, these stem from fundamental uncertainties concerning the future markets, the availability of renewable electricity and the capacity of the electricity grid. Moreover, high investment costs combined with a lack of financial and fiscal incentives and low carbon prices make the business case for electrification currently unattractive. In addition, the high degree of systems integration onsite and the baseload nature of the cracking process make the implementation of electrification challenging. Government policy could play a pivotal role in resolving these barriers. The purpose of this research is therefore to explore policy options at a national level that enable an acceleration in the chemical industry’s electrification while being robust against uncertainty. For this purpose, the Robust Decision-Making (RDM) framework was applied. As part of this framework, a system dynamics (SD) model was built to describe the behavior of the socio-technical system of the naphtha cracking industry. This model was then subjected Exploratory Modelling & Analysis (EMA) to simulate the impact of various policy options on the electrification rate against a large number of plausible future scenarios. Moreover, a robust policy search was conducted using a multi-objective robust optimization algorithm.The analyses of the simulation results suggest that profound changes in current policy instruments are required to achieve a significant degree of electrification in the naphtha cracking industry with sufficient certainty. It is advised to increase government funding for electrification, implement a fiscal shift away from gas towards electricity and to increase the effective carbon levy imposed on the industry. However, though the analyses show that government policy has a significant impact on the development of electrification, its success appears conditional on external factors, most importantly, developments in the energy and carbon markets. In fact, the electricity price and the ETS carbon price turn out to be more influential on emission reduction than most policy interventions. The availability of renewable electricity is the single most influential factor affecting CO2 emissions and hence, is considered paramount in achieving emission reduction. Therefore, policy aimed at accelerating electrification should be accompanied by decisive government action regarding the development of renewable electricity sources.

Energy transition has become the lead focus of the European Union (EU) agenda. The introduction of European Green Deal (EGD) has further raised the targets regarding greenhouse gas emissions, Renewable Energy (RE) and Energy Efficiency (EE) and strengthened legislation concerning the circular economy and biodiversity. Forest biomass energy has been at the forefront of the EU policy-making. In Central and Eastern Europe (CEE), where the forest biomass is mainly used for domestic heating in the form of round-wood, the issue of AQ has a significant role in the energy transition agenda. The burning of round-wood in old low-efficiency stoves cannot produce sufficient energy while exposing people to indoor and outdoor pollution. Moreover, the Particulate Matter (PM) from wood burning is associated with multiple health conditions. The research aims to understand the impact of the forest bio-energy policy mix on EP; hence the following research question is formulated: How is the forest biomass energy policy mix affecting energy poverty alleviation in Sofia, Bulgaria? The case study approach is suited to answer the main research question. Furthermore, the case study approach allows for in-depth exploration of complex issues and uses multiple research methods. The theoretical framework of the research is based on the policy mix conceptualisation proposed by Rogge and Reichardt (2016). Rogge and Reichardt define three building blocks of the policy mixes: elements, process and characteristics. The conflicts existing between the national level policy instruments and the rest of the policy mix result in vertical and horizontal inconsistency. Nevertheless, synergies between the EU instruments and the appliance change programs result in synergies. The lack of capability of the local and national institutions and the perceptions conflict indicate a lack of policy mix coherence. At the same time, the policy mix does not have any instruments to cover market failures exposing households to higher energy cost and include policy instruments outside the national energy plans indicating a lack of comprehensiveness. While the lack of consistency, coherence and comprehensiveness have some positive impact on energy cost, thus reducing energy poverty, the conflicts in the policy mix are sustaining the use of traditional fuels. Overall, in the situation of surging energy prices, the forest bio-energy policy mix is sustaining the EP; however, in the case of lower energy costs, the existing conflicts constitute a significant obstacle in alleviating EP in Sofia. The evaluation of the impact of the policy mix on energy poverty in Sofia shows that the lack of constancy, coherence and comprehensiveness and credibility does not always lead to adverse effects on the energy poverty indicators. Nevertheless, the results support the findings of other articles focusing on the impact of the energy transition on energy poverty and that the way to achieve energy poverty alleviation is to strive for consistent, coherent and comprehensive energy transition policies. In terms of the policy mix concept, the study shows the need for further empirical research on the multi-dimensional policy mixes and more emphasis on the instrument’s design features.

Increasing demand for passenger services in densely populated urban environments, are currently covered overwhelmingly by private vehicles. Their impact on CO2 emission, present a serious obstacle to the reduction objectives, in the Netherlands alone the target of 45% by 2030, for limiting the global warming to 1.5°C degrees. Autonomous Vehicles (AV) and Ride-Sharing services are believed to be offering a crucial technological and perception shifts to reducing emission. In this work, a methodology for assessing the impact of a large-scale AV fleet ride-sharing system to replace the one-two passenger vehicle traffic using Rotterdam as the case study is designed and proposed. The approach includes three stages: 1. Building and finetuning a traffic model using publicly available data 2. Designing and implementing a trip merging component, in the form of two distinct heuristic greedy algorithms and a variation of the second one, using Python programming language. 3. Evaluating the impact of each merging scenario on the network in SUMO. The system’s influence and results are driven from the deployment of the ride-sharing service on the 2016 traffic model. The decrease in total number of trips, vehicle kilometres travels, and subsequent improvements in traffic flow resulted in 39% reduction in CO2 emission with the third algorithm. This result not only establishes the extent of AV ride-sharing service’s potential for emission reduction and traffic quality improvement. This adaptable methodology also operates as a proof of concept for a preliminary step for policy makers when considering implementing such service in any urban setting. Two of the major elements not included in this research are multimodal travel, like combination with public transport, and changes in demand for each mode choice based on traveller’s behaviour. These elements thus remain open for future consideration.

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.

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 Dutch Government has prioritized the construction sector for a circular economy transition. This research aims to understand the circular transition in the context of the Netherlands’ construction and demolition waste management system. By using a mix-methods approach based on systems analysis and a material flow model, this study assesses the environmental impacts of 7 identified policies. The results found that implementing mandatory pre-demolition audits performed the best in increasing the upcycling and reuse rate from 8% to 23% by not only focusing on source reduction but also prioritizing reuse. The policy measure which focuses on waste identification, separation, and collection at the source with sorting obligations performed the best to recover the most amount of waste (20.3 million tonnes/year) and increase the total recovery rate to 83%. A scenario analysis done by clustering policies for a strategic focus found that recovering more waste on its own will not reduce carbon emissions in the long run unless it’s redirected towards low-carbon destinations. A recommendation to design effective circular policies for waste management is to include carbon emissions and upcycling and reuse-based indicators. From an academic perspective, this study integrates a partial life-cycle assessment into a material flow analysis by integrating carbon emissions into a material flow model. By synthesizing and using a multi-criteria framework to calculate potential impacts, this research adds to a growing literature in policy design. The mix-methods approach used can also be adapted to other waste-based industries with a similar supply chain background.

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.

Traffic congestion has proven to be a wicked problem that is affecting people's economy and health. Until now, traditional policies have not yet been able to solve the problem entirely. To tackle such a challenge, new innovative measures are needed. Mobility as a Service (MaaS), a new paradigm in mobility where all modes of transportation are offered to the user in packages through a digital platform, offers a new approach with a service that could compete with private car ownership, the main cause of traffic. However, there is no policy research on the implications of MaaS and whether an intervention from the government is necessary to boost the reduction in traffic congestion in this new paradigm. MaaS is a system full of uncertainty and it's adoption is a time changing complex process. System Dynamics is an interesting method to close the research gap identified, since it is commonly used in time changing systems with high uncertainty. However, this is a method not commonly use for the study of mobility, and there is a need to identify the challenges when using this method. The main objective of this thesis project is to evaluate the use of System Dynamics as a research method to understand MaaS.

A System Dynamics study towards the potential effects of interventions on the food waste produced by households. Households are a major contributor to the waste of food within the Netherlands. There are negative consequences regarding the waste of food. A causal relation diagram is created, a set of interventions is defined, and based upon a System Dynamics model is created. Based upon the simulations, it is concluded that the process in which food is wasted is fragmented. Reducing the waste of food effectively demands combining multiple interventions. There are two important aspects within a household when multiple interventions are applied. Interventions should focus on the 'willingness' and on the 'knowhow' within a household. Focusing on both aspects results in a synergy. The combined effects of the interventions is bigger than the individual effects accumulated.

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.

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.

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.