In light of transitioning towards a sustainable, low-carbon energy system, NdFeB (Neodymium-Iron-Boron) permanent magnets are set to play a crucial role. These magnets, currently the strongest available, are integral to the functioning of wind turbines, electric cars, and various digital appliances. Their superior performance is attributed to the inclusion of rare earth elements (REEs). NdFeB magnets contain four REEs: Neodymium and Praseodymium, categorized as Light Rare Earth Elements (LREEs), as well as Dysprosium and Terbium, classified as Heavy Rare Earth Elements (HREEs).
There is a general consensus among researchers that the amount of REEs available in the earth’s crust suffices to meet the growing demand for NdFeB magnets by the energy transition. A potential bottleneck issue is therefore not the availability of REEs in the earth’s crust, but the timely supply of REEs needed for NdFe B magnets under acceptable prices. China’s dominance in NdFeB magnet production presents a substantial risk to the widespread adoption of wind energy and electric vehicles.
This study employs a model-based analysis to explore the potential of substituting REEs in NdFeB magnets within the NdFeB supply chain and clean energy technologies. To accomplish this, a combination of System Dynamics (SD) and Exploratory Modelling and Analysis (EMA) is employed to model the system and subject it to a wide range of scenarios. The assessment of the long-term demand and supply of materials supply chains is a complex and deeply uncertain issue. SD is tailored to model intricate systems with time delays, accumulation, internal feedback loops, and non-linear behaviours. To address the inherent uncertainty in the system, EMA is utilised to provide decision-making support under deep uncertainty through computational experiments.
The global supply system for NdFeB magnets primarily consists of several key elements, including the supply chain, demand, primary production, recycling, and the substitution of REEs. Primary REE production encompasses both legal and illegal activities, with a significant portion of HREEs being extracted through illegal means. The demand for NdFeB magnets and the REEs required for their production emanates from various industries, including electric vehicles, wind turbines, computer hardware, and other applications. To meet this demand, there are several potential methods for substituting REEs, including element-for-element, process-for-element, magnet-for-magnet, and component-for-component substitution. These substitution methods are influenced by factors such as the average periodic price of REEs, potential cost reductions in the case of process-for-element substitution, a substitution threshold for magnet-for-magnet substitution, and various considerations, including power density and technology readiness, for component-for-component substitution.
The study's findings indicate that the electric cars, wind turbines, and REEs in use will follow a similar pattern characterized by a significant level of uncertainty. In the most conservative scenario, there is a slight increase until 2050, while in the most optimistic scenario, there is exponential growth.
The most influential uncertainties affecting the quantity of electric cars and wind energy are driven by intrinsic demand stemming from the energy transition, product lifespan, and the production system's ability to adapt rapidly to changes in demand by scaling up or down. Differences in product lifespan are especially pronounced in the early stages of the simulation due to relatively low growth in intrinsic demand during that period. However, in the later phases of the simulation, intrinsic demand becomes the predominant influencing factor.
Both wind energy and electric cars demonstrate a positive relationship with the quantity of REEs in use. Nevertheless, scenarios exist where a relatively high number of wind energy systems and electric cars can be developed without an exceptionally high utilisation of REEs.
Substitution has the potential to temporarily alter demand or induce a permanent shift in the demand curve, depending on price levels and substitute product quality. Short-term demand for technologies and materials tends to remain continuous, with fluctuations in response to price changes. However, introducing new technology or changing production processes can lead to a permanent shift in the demand curve. Elevated prices incentivize firms to explore alternative technologies, leading to the development of superior products.
The study's findings suggest that both element-for-element and process-for-element substitution have the potential to induce both temporary and permanent shifts in demand. Magnet-for-magnet substitution is a more temporary solution, while component-for-component substitution often results in a more permanent change.
The development of alternative technologies that do not rely on NdFeB magnets has the potential to significantly impact the number of wind turbines and, particularly, electric cars without NdFeB magnets. Such advancements can lead to more permanent forms of substitution and contribute to increased adoption of these technologies. While currently available alternatives can also serve as substitutes for NdFeB-based cars, alternative technologies for electric vehicles that are still in development might have the potential to significantly reduce the number of electric cars utilizing NdFeB magnets.
In conclusion, this research suggests that substitution is unlikely to play a central role in securing a sufficient number of electric cars and wind turbines for several reasons:
1. The Earth's crust contains an ample supply of REEs, which should be sufficient for producing the necessary number of electric cars and wind turbines, at least until 2050.
2. Although REE production may lag behind demand, the process of substitution takes time and can only offer limited short-term flexibility. Moreover, short-term disruptions have a relatively minor impact on long-term supply.
3. Alternative technologies with reduced or no reliance on REEs do not significantly outperform their REE-intensive counterparts, thereby not leading to an increase in demand.
This is not to say that a substantial number of electric cars and wind turbines can only be produced with a large quantity of REEs. There are scenarios in which the utilisation of NdFeB and REEs remains relatively low, yet a considerable number of electric cars and wind turbines can still be manufactured.
While the importance of transitioning to a sustainable, low-carbon energy system is evident, the likelihood of a substantial number of electric cars and wind energy systems being in use by 2050 primarily hinges on demand for these technologies. Other factors such as technological advancements, price reductions of wind energy and electric vehicles, government policies, and incentives can also stimulate this demand.
Recommendations for future research include the use of this model for other critical metals. The extension of this model make it relatively easy to use this model to evaluate the substitution potential of critical metals in other technologies. Especially when metals are a vital component of an intermediate product that is used in the end-product, then the structure of this model would be directly applicable.

The Netherlands is on the verge of initiating its most extensive maintenance project in history. The Dutch government will have to invest an estimated minimum of 260 billion Euros for the renewal of its aging civil structures like bridges, viaducts, and tunnels. This large maintenance operation is labeled as the replacement and renovation task. Within the replacement and renovation task, so-called ‘Baby Boomer bridges’ require the most attention. These bridges, accounting for a substantial portion of the 85,000 bridges in the Dutch inventory, face challenges stemming from prolonged stress and corrosion issues. The Merwedebrug's narrowly averted catastrophe in 2016 served as a wake-up call, highlighting the critical need for proactive and strategic maintenance practices. A key challenge in the replacement task is the limited construction capacity, especially with an expected surge in maintenance between 2040 and 2080. Efficient and systemic asset management is pivotal in minimizing the required capacity increase and to help ‘flatten the curve’.

One of the core strategies identified by the Dutch government to facilitate the transition towards more systemic and efficient infrastructure asset management is to cluster bridge maintenance projects. Project clustering involves consolidating multiple maintenance projects with similar characteristics or geographical proximity into one portfolio. Existing research has highlighted the positive impacts of project clustering on project-specific performance indicators. However, there is a lack of studies examining the broader implications of different clustering strategies on the entire transport system. The current literature predominantly comprises retrospective studies analyzing historical project data, providing valuable insights into project clustering effectiveness but falling short of assessing its influence on future infrastructure system behavior. This research aims to bridge this gap by conducting an exploratory modeling analysis to explore the effects of different maintenance cluster strategies on the performance of the transport infrastructure network.

This thesis employs Entity-based System Dynamics (SD). Entity-based SD is a relatively new modeling methodology and can be seen as a combination of agent-level (ABM) modeling and macro-level (SD) modeling. This combination allows for the modeling of the (spatial) behavior and attributes of individual bridges, roads and regions, while retaining the capability of doing macro-level analyses. Furthermore, as the bridge maintenance problem is subject to deep uncertainty, Entity-based SD was paired with the Exploratory Modeling and Analysis methodology. This allows for the exploration of the repercussions of various combinations of assumptions about uncertain factors in the system. To allow for the combination of the two methodologies, a novel EMA Workbench-Ventity connector was constructed for this thesis.

Because Entity-based SD is still a relatively novel methodology, there is a lack of spatially explicit applications within the existing scientific literature. As such, this thesis pursues two objectives, (1) developing, and reflecting on the added value of a novel spatially explicit Entity-based SD modeling method when modeling the effect of bridge maintenance cluster policies on the wider infrastructure system, and (2) identifying maintenance cluster policies that are effective at facilitating a steady and predictable maintenance capacity demand. To pursue these two objectives, an abstract network was constructed using the Entity-based System Dynamics methodology.

The analysis of the model outcomes shows that the model was able to generate spatially explicit relationships between traffic flows and bridge degradation. The model was also able to capture the performance of maintenance cluster strategies and showed expected behavior. Six policies were tested with the model, three variations of geographical clustering (small, medium, and large), construction type clustering, construction year clustering, and a no clustering policy. The model results indicate that larger maintenance clusters bring about more fluctuating changes in capacity utilization, while smaller clusters lead to a higher total number of expected projects over a 100 year simulation period. Larger clusters also result in a higher average load capacity for the bridge set, mainly due to increased preventive maintenance. Despite larger clusters generally outperforming no cluster policies, their overall effectiveness is diminished, especially concerning the critical outcome of change in capacity utilization. A geographical cluster policy with small clusters, an average of 1.9 bridges per cluster, stands out for its more stable maintenance capacity utilization compared to a no cluster policy option and slightly better performance in other key outcomes.

As such, policymakers should implement policies that encourage the formation of small maintenance clusters. However, as the network specification plays a crucial part in the performance of cluster policies, policymakers should adopt a flexible approach, considering the specific characteristics of the infrastructure network when formulating maintenance clustering policies. Future applications for the model could add additional external effects to the model, introduce finite maintenance capacity and a finite maintenance project size, or include dynamics in the model that allow for the modeling of traffic jams.

At a methodological level, it can be concluded that Entity-based SD is a suitable approach to infrastructure modeling. The added value of the novel spatially explicit Entity-Based SD approach can be described in five points. First, the method holds a high degree of replicability. Because entity types can be independently defined, infrastructure components can be individually modeled and reused in other models. Additionally, the method makes use of externalized network initialization data, which separates the dynamics of infrastructure components and the network specification data, allowing for components to be altered individually without the need to alter the other. Second, the computational requirements of the approach are limited compared to other infrastructure modeling approaches. Third, similar to SD modeling, the model is made up of a clear model structure with stocks, flows, and causal links which enhances communicability and supports group model building with stakeholders. Fourth, as an extension of SD modeling, the method provides a holistic approach to infrastructure modeling, which means that it enables the modeling of not only individual infrastructure components but also the broader system in which these components operate. Lastly, the approach is capable of coping with high degrees of uncertainty due to the EMA Workbench connector that was constructed for this thesis.

Based on the arguments presented in this thesis, the novel spatially explicit Entity-based SD approach is considered to be a suitable new avenue for infrastructure modeling. However, this study should be considered as a first investigation into this approach and is therefore incomplete. Although the abovementioned added values have been identified during the course of the thesis, some limitations and future improvements still exist. Firstly, the Entity-based SD methodology offers limited documentation, as the maturity of the approach is low. Secondly, the approach is not meant to generate precise forecasts. As such, if this is the objective of the modeler or stakeholder, Entity-based SD should not be considered as a candidate approach. Future applications of this modeling approach could look at the performance of the approach when modeling larger networks. Therefore, spatial Entity-Based SD should not be seen as a replacement for current infrastructure modeling approaches. Rather, it should be viewed as a new addition to the scientific field of infrastructure modeling.

The Netherlands is subject to labour scarcity across a variety of industries. While labour scarcity has occurred in the past, only now the consequences of higher educational attainment have become prevalent in the Netherlands. Due to a shortage of vocational workers and a population that is getting older, circumstances ask for adequate policy-making. Although policies like increasing the formal retirement age are put in place to negate labour scarcity, it remains difficult to understand the long-term outcomes of policy implications made in the present.

Understanding this long-term behaviour of the dutch labour system is deemed useful to develop strategies that result in a well-functioning labour market. Since the labour market is intertwined with other systems (e.g. population, educational system and the economy) and is subject to uncertain future developments it results in increased complexity of understanding long-term dynamics. This research aims to develop a better understanding of the long-term behaviour of these systems in deeply uncertain futures.

The Netherlands is looking to decarbonize its economy and replace natural gas from Groningen and Russia with different energy carriers. Dutch government has been mainly stimulating renewable electricity production, CO2 capture and storage (CCS) and sustainable heat generation (RVO, 2022) to address these challenges. While these technologies are an indispensable part of CO2 reduction strategies, for full decarbonization of the economy more advanced technologies (Hammingh et al., 2022), new materials, mitigation solutions and negative emission technologies are required (Yang et al., 2022). To both reach full climate neutrality, and to replace Russian gas with green alternative, a renewable molecule is vital. The main alternative for fossils that is currently deployed is green electricity. Green electricity is an effective way to replace fossil fuels, but not all industries can replace their liquid or gaseous fuels with electricity. For those industries the Netherlands envisions green hydrogen to be the sustainable alternative. In their climate agreement the Dutch government has set the goal to have 3-4 GW of green hydrogen production via electrolysis installed in 2030. Currently, this goal seems difficult to achieve as the green hydrogen industry struggles to kick off. This is in large due to the fact that there is a gap in literature and policies between research projects and full scale green hydrogen production. Policies are needed to accumulate economies of scale to reach a price level at which green hydrogen is competitive with fossil alternatives or other forms of low-carbon hydrogen. The goal of this research is to identify a robust policy strategy that will enable the Dutch industry and government to reach the goals for hydrogen production...

Celsius or 1085 GtC. This thesis explores this significance and points toward prioritizing ecosystem conservation as a highly effective strategy for mitigating climate change. Initiatives such as carbon offsetting, performed by organizations like ‘8 billion trees’ and ‘cool earth’, could provide feasible solutions to enhance carbon sequestration. The EU's Nature Restoration Law is a step in the right direction, advocating for the conservation of existing ecosystems and restoring deteriorated systems, specifically increasing organic stocks in forests and restoring and rewetting drained peatlands of agricultural use and peat extraction sites seem to be compelling aspects in terms of improving the carbon system proposed in this law. I encourage the exploration of similar policy changes globally, underscoring the importance of taking immediate action and ensuring no further deterioration of existing terrestrial carbon systems.

Following the importance of terrestrial carbon storage, the debate remains about who should sacrifice their land. This research has displayed a high sensitivity in land allocation for terrestrial carbon storage when comparing ethical perspectives of justness, also defined as climate justice principles. This sensitivity can be seen as normative uncertainties and represent potential tensions in developing a coherent global policy. In my research, certain countries are more sensitive to these normative uncertainties than others. The Netherlands, Pakistan, China, and Yemen are highly sensitive to terrestrial carbon storage obligations if islands and smaller states are not considered. In the case of the Netherlands, this sensitivity is caused by a high GDP and, therefore, ability-to-pay. Still, when following efficiency prioritized, Dutch agricultural land should be kept for its high agricultural productivity. China is expected to reach a high level of domestic food security. Therefore, they would have to sacrifice their land for terrestrial carbon storage according to a different interpretation of ability-to-pay. Also, the efficiency prioritized principle for China would be ideal because, with their economic growth and innovation, they are expected to reach a high level of agricultural productivity. In the case of Yemen, it is different, Yemen has low agricultural productivity, and efficiency-prioritized will lead to them converting agricultural land to forests. The best interpretation of ability-to-pay for Yemen is the available land that could be converted for terrestrial carbon storage.

In my analysis, I showed proof of principle to include these normative uncertainties in policy analysis to explore areas of consensus. Through this approach, I found countries that should convert or conserve their land for terrestrial carbon storage and countries that arguably could still replace their forests and wetlands for other land covers such as urban- and agricultural land. I propose that climate justice principles should be included as uncertainties in policy-analysis, thus arguing for – additional – simplified models to allow for easy implementation of these uncertainties in the analysis.

To come to these findings, I performed an extensive analysis of the global food-, land cover-, and carbon system, simulating a wide range of scenarios up to the year 2100. The model I have developed operates at a national-detail level and includes 173 countries worldwide. The model considers several factors for each country, such as socioeconomic development, current land cover, agricultural productivity, and food demand. The national systems interact globally to represent global systems such as food change and the carbon cycle.

I tested a wide range of distribution methods on this system for terrestrial carbon storage burden, represented by five policy levers. Additionally, I considered 21 uncertainties in the model and performed 10.000 experiments to ensure the robustness of the proposed policies. In the last phase of the analysis, I created world maps that display the land countries should convert for terrestrial carbon storage according to climate justice principles and the sensitivity per country. These maps allow for an easy understanding and interpretation of the climate justice principles.

I started the research by exploring frequently used climate change policy evaluation in ethics. Ethics literature discusses two main social justice types: distributive and procedural justice. In this study, I focus on distributive justice for its applicability in modelling solutions. I utilize pre-defined climate justice principles and introduce a new principle – efficiency prioritized, based on the utilitarian principle. The ‘principle efficiency prioritized’ allocates agricultural land to countries with the highest agricultural productivity and terrestrial carbon storage burden to the less-productive countries. Selected principles are Ability to Pay (based on available land for terrestrial carbon storage, domestic food supply, and GDP per capita), You-Broke-It-You-Fix-It (based on historical emissions), and Efficiency Prioritized, with different interpretation methods considered for Ability to Pay.

I modeled the system using Vensim, a System Dynamics modeling software. System Dynamics is a quantitative modeling formalism suitable for dealing with complex systems, allowing for exploring relations between system structure and behavior. It helps to understand the complex land use, land-use change, and forestry, food, and carbon system. It also enables the incorporation of parametric and structural uncertainties, testing a wide range of scenarios in systems with deep uncertainty. The model consists of several sub-systems, leveraging existing models from prior research by Auping (2018), and takes data from sources such as the World Bank, FAOSTAT, OECD, and IPCC as input.

To find robust policies in the face of deep uncertainty, I adopted an Exploratory Modelling and Analysis (EMA) approach using the open-source EMA workbench for Python. This approach involves performing a broad range of computational experiments, analyzing the results, and identifying robust policies based on these findings. EMA workbench has a special connection built-in to run experiments with Vensim efficiently. It includes built-in functions for sampling, such as Latin Hypercube Sampling, and scenario discovery, such as PRIM, used in my analysis to find the scenarios of interest.

I visualized the results with Basemap, NumPy, Pandas, and the EMA workbench, among other tools. Creating maps allows for a simple data representation, enhancing understanding and communicating the findings. This approach enables a thorough exploration of climate justice principles, modeling, and experimentation, providing valuable insights for decision-makers facing complex and uncertain climate change challenges.

To summarize, I applied System Dynamics modeling and scenario discovery to explore the global food and carbon system. I proved the importance of terrestrial carbon storage, especially conserving existing forests and wetlands. Terrestrial carbon storage policy also faces policy challenges in implementation, mainly the distribution of the terrestrial carbon storage burden. Because land for terrestrial carbon storage goes at the cost of agricultural and urban land that could provide economic prosperity and food security, there is a trade-off between different Sustainable Development Goals. Several distribution conventions have been evaluated following climate justice principles to find overlapping policies between the principles. I found a significant difference in the distribution of terrestrial carbon storage obligations between the principles, displaying potential complexities and tensions in policy-making. I also found consensus between the principles, defined as non-discriminatory policies. Policy-makers should start with non-discriminatory policies to ensure swift implementation. I recommend that other policy modelers also include ethical perspectives as uncertainties in their models to find non-discriminatory policies and, if necessary, develop simplified models to enable this.

With growing tensions in today's world, new powers rising and contesting old powers, two nations risk ending up in a so called security dilemma: One nation's increase in security threatens its opposing nation. The consequence could be the escalation of military buildups into arms races. With ongoing technological advancements, militaries around the world are expanding on autonomous capabilities that need less and less human control. In this research, I investigate under which conditions arms races for these systems between two fictitious nations may occur and how the nations can influence these. For this, I construct a system dynamics model based on literature and expert consultations and conduct experiments to generate a wide range of possible scenarios. The model results suggest that different types of arms races for autonomous military capabilities occur under specific conditions: the inferior nation lacks behind the superior nation and fills this initial gap not with a greater number of autonomous systems, but with higher quality autonomous systems, leading to an arms race in quality. Bilateral disarmament in the form of regulations on the level of autonomy seem not to be the answer for the superior nation: some of the arms racing is reduced, but the superior nation now loses both racing and non-racing scenarios. Restrict an opponent in their technological development is more effective for the superior nation. Still, the inferior nation is able to win in the specific case, where the superior nation requires "too much" human control. Generally, arms races can further be reduced when both nations avoid to be too "reactive" to each other's arming actions. This suggests, that a third party could step in and aim at relaxing the tensions between the two nations. Further, this third party could also aim at encouraging the inferior nation to raise its ethical standards. To conclude, the model can give insight into which decisions a nation or a third party can take with different goals in mind. These include, for the nations involved in a competition as a result from tensions, to either win the race towards autonomy or to prevent its opponent from winning it. A third party, interested in a peaceful world without having nations piling up autonomous military capabilities, might aim at preventing arms races whatsoever.

Since the conception of the euro, it has often been suggested that the eurozone start a programme of jointly issuing government debt. There are many arguments for these so-called Eurobonds, and temporary issuance of them has already taken place in response to the economic impact of the recent COVID-19 pandemic. However, a permanent Eurobond programme remains a controversial topic within the EU.
Advocates claim that a Eurobond programme can facilitate economic growth and stability for the entire eurozone. It could offer much-needed fiscal space to some of the eurozone’s most heavily indebted southern economies, by decreasing borrowing costs. It is assumed that the extra growth facilitated in these countries will spill over to the rest (mostly the less indebted north) of the eurozone, through increased cross-country trade and investment. Criticism mostly originates from the more financially sound eurozone member states, which fear a situation of moral hazard that leads to uncontrolled spending, and a possible deterioration of their own creditworthiness.
This research attempts to explore the economic effects of the introduction of Eurobonds on the eurozone economies. For this effort, two distinct types of Eurobonds are identified and analysed: unlimited Eurobonds and Blue Bond Eurobonds, with the former allowing unlimited issuance of mutualised debt, and the latter restricting this to 60% of a country’s GDP.
The economic sub systems that are crucial for explaining the dynamics relevant to this study show a high degree of complexity and uncertainty; whether it is the dynamics of debt management (including stocks, flows, delays and feedback mechanisms), the different ways of determining bond yields (these are structural uncertainties), or the magnitude of economic elasticities/sensitivities (these are parametric uncertainties). These system characteristics stress the need for a simulation modelling approach. Because the interactions between the system’s components and the relative importance of certain elements are of interest, and because the results should be insightful to non-technical policy-makers, a transparent, ‘white-box’ model can provide the most meaningful results. For these reasons, a system dynamics model has been developed for this study.
Specifically, an exploratory system dynamics modelling and analysis approach is used, due to the inherent structural and parametric uncertainties related to the system that have to be accounted for. Through exploratory experimentation with this model, the effectiveness of both Eurobond policies has been assessed based on how they affect eurozone and country-specific GDP, interest rates, and debt levels. All experimental results are robust for the different structural and parametric uncertainties identified in the system; multiple methods of determining bond yields taken have been into account, just as a large number of configurations of the parametric uncertainties.
As for the economic growth facilitated by both Eurobond policies, the country-level results are mostly insignificant (i.e., not more than 0.50% additional GDP growth after 10 years). Only slight increases in GDP are observed for both policies, with the benefits not evenly distributed amongst member states. Southern, more heavily indebted economies have more to gain, due to the larger relative decrease in their borrowing costs, assuming that the additional funding is spent productively. Furthermore, the anticipated positive economic spill-over effects caused by increased cross-country trade and investment (and not attributed decreases in interest rates) are shown to be insignificant as well.
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However, the results of the analysis do indicate that both unlimited and Blue Bond Eurobonds have the potential to offer significant benefits to the eurozone’s economic stability, by reducing borrowing costs for all participating countries. As anticipated, it is again mostly the heavily indebted southern economies that benefit from this. Unlimited Eurobonds offer slightly more benefits to these countries, when compared to Blue Bond Eurobonds. However, the latter is the preferred option for the less indebted north, since it restricts debt issuance and subsequently prevents Eurobond yields from surpassing their current borrowing costs; something will eventually be the case with unlimited Eurobonds.
Based on this study’s results, a recommendation is made to start the implementation of (a variant of) the Blue Bond Eurobond policy. It has the potential to significantly increase the eurozone’s public debt sustainability, while also offering minor (mostly insignificant) growth benefits to the heavily indebted southern member states, without deteriorating the creditworthiness and the fiscal position of the other, northern member states. Furthermore, the existing market pressure for prudent fiscal behaviour is maintained in the Blue Bond Eurobond proposal, mitigating the risks of moral hazard feared by critics of debt mutualisation.
Additionally, it is necessary to set up an adequate legal and political framework that incentivises fiscal discipline and does not allow for free-riding behaviour. A transfer of fiscal sovereignty to the EU level is to be a requirement for participation in the Eurobond programme. This is necessary to build confidence in the system, both for the eurozone member states, as for the capital markets and the general public. It is further deemed sensible to include a legal clause in the Eurobond programme that allows for the suspension of individual member states’ participation. The (budgetary) conditions for such a clause to be invoked, and the potential (financial) penalties associated with this remain to be decided upon by the eurozone member states and the EU’s (to-be erected) fiscal and monetary institutions.
Keywords: Eurobonds, Bond yield determination, Debt sustainability, Macroeconomic analysis, Policy analysis, Exploratory Modelling and Analysis

It is no secret to hospital and public health managers thatresource shortages worsen pandemics. The importance of preparedness has longbeen recognized within the European Union. One of the current H2020 innovationprojects in this domain is PANDEM-2, aiming to improve pandemic preparednessfrom the side of resource management and sharing by creating cutting-edgedigital tools. As part of these tools, a system dynamics (SD) healthcareresource model is being developed, with the ultimate goal of embedding it in adashboard accessible to pandemic managers. This is done in order to supportmanagers in rapidly making evidence-based assessments and decisions, or as inthis thesis shortened, to provide situational awareness. In short, the specificproblem we were tackling was the exploration of how can pandemic preparedness canbe achieved via current healthcare resource models and how a specific resourcemodel (developed by a previous intern) can be used. First, to gain a generalunderstanding of the state-of-the-art models, we looked into the scientificliterature from two directions: We looked at how existing resource models work,are validated, and are used via literature review. For another perspective, welooked at scientific frameworks describing modelling and validation to informour methodology. Therefore, this thesis seeks to answer the question: How tosupport healthcare resource managers in acquiring situational awareness via anSD model? To gain a better understanding, we did a literature review first tounderstand how others approach the topic of healthcare resource modelling.

 

We first analyzed the existing scientific literature by apreliminary search, which was also used to construct a more detailed andrefined second search. In this second search, we used the PubMed database tosearch for articles containing the keywords hospital and healthcareresource, pandemic, model, validation, andsynonyms. Then the returned articles were screened for relevancy, resulting ina total of 25 healthcare resource models analyzed. Within these analyzed models(and articles), we found that the most common approach is using SD models, andthe second most common approach is using regression models. Roughly two-thirdsof the models fall into these two categories. Furthermore, we found that there isa stronger focus on hospital resources than public health resources and that nocommon approach is used for model validation. We also found that the articlesdemonstrating that the model is used to support real-life decision-making wereusually not about SD models; therefore, examining how to use SD healthcareresource models for decision support is not mainstream. We also found that themodel used in our research is novel in the sense that it encompasses resourceson a more detailed level than existing published models.

 

To further our understanding, we decided to answer ourresearch question by holding a workshop, where we examine how to communicatemodel outputs. While examining the relevant modelling methodologicalframeworks, we defined the tasks that need to be done in this thesis throughthe lens of the modelling cycle. We need to perform the tasks of verification,validation, and holding a workshop, which partly encompasses evaluation. Thenexamining the literature about verification and validation, we encountered theimplication of a well-known philosophical problem of scientific theories' formodelling: It cannot be demonstrated whether the model (or the theory) is atruthful description of the phenomenon.

 

To overcome this problem, in modelling, validation refers tobuilding confidence that the model is fit for its purpose. In this study, thepurpose of the model changed from describing the different mechanisms foundimportant to generate semi-realistic outputs to be used in the workshop;therefore, it had to be revalidated. This was addressed by performing aparticular set of relevant validation tests. The model passed verification andthen the validation for this purpose, so we continued with the workshop. Wedecided that in the workshop, we would use a presentation to communicateintervention opportunities for the pandemic based on the model outputs. Thenafter each intervention, the participants were asked to evaluate the easinessof understanding the output and to talk about what actions the presentedinformation inspire.

 

By holding the workshops, we found several relevant facts:First, it was found that the goal participants were searching for was to getrid of the perceived gap. This also meant they were searching for insights thatcould be used for operational planning purposes. Furthermore, the analysis doesnot need to stop at visualizing outputs. One of the participants indicated thatfurther analyzing the graphs is not as easy for them as for an analyst workingwith the model. We have also seen that participants tend to augment thepresented data with their experiences, which (unless explicitly presented)leads to assumptions about how the model works. Some participants also pointedout that the contact tracing part of the model is already outdated (in lessthan a year). We have identified some practical ways to avoid ambiguity whilecommunicating about healthcare resource models. First, we found that despitethe insights we gained by analyzing model outputs were not novel, thediscovered scenarios were still good discussion starters in the workshop. Thisis likely the mechanism of the scenarios acting as a reminder for passiveknowledge, which participants subsequently shared. Furthermore, extra careshould be taken to explain the context of how the data got generated, especiallyconcerning the model. As the presented data left some space for interpretation,participants sometimes had different assumptions than the ones coded into themodel. While these could be resolved in the workshop to some extent, this willnot be the case for the dashboard. Given some familiarity with the audience, itis possible to expect some questions and misunderstandings, which could beproactively addressed in a description or in a `frequently asked questions'. Wealso identified two presentation types that were easier to process thanpresenting key model outputs: The first option is to analyze key model outputsfurther than graphing and present the key insights (such as peak resourcedemand) in a tabular format. Alternatively, the second option is to build allvisualization on the same template and explain that template on the firstoccurrence in detail. In subsequent occurrences, it should be enough to pointout only the interesting parts and give participants time to process theinformation.

 

From another perspective, participants expressed a need fordata that can be used for planning purposes. However, given the uncertaintyabout the system, these, as we call consolidative models, cannot be constructedyet. While exploratory modelling is an alternative SD technique for addressingdeep uncertainty, it does not attempt to produce numerically accuratepredictions. However, from a novel perspective, the consolidative andexploratory approaches can be viewed as two ranges on the spectrum ofuncertainty about the modelled system. Viewed from this perspective, validationmeans reducing uncertainty about the system. Nevertheless, to achieve theconsolidative models, datasets about resource usage are needed, but as far aswe know, no such dataset exists. As data to create such datasets is probablyalready being collected for operational purposes, it is likely that thecollection and aggregation of these data are not happening. However, creatingsuch datasets comes with some challenges. There is a value trade-off betweenprivacy and preparedness through data collection, and the current datacollection techniques are unlikely to be unified. Overcoming these challengeswould need quite a significant upfront investment. To answer our originalquestion of how to support healthcare resource managers in acquiringsituational awareness, this thesis argues that, by far, the biggest utilitycould be achieved by strengthening data collection and aggregation, as itenables the possibility to develop surrogate models. However, as this requiresa significant upfront investment, question-driven exploratory models remain analternative way to address these uncertainties.

The energy transition makes us less dependent on fossil fuels, but it also brings a new dependence to the table: a dependence on critical metals for green technology. This study used an Exploratory System Dynamics Modelling and Analysis approach to analyse the transition to a circular lithium system towards 2050 and the potential effectiveness of the Battery Regulation. It was shown that the Battery Regulation is likely to increase the end-of-life lithium recovery rate. However, it is yet unsure whether the set of policies is robust in preventing recycling from coming at the cost of lifetime-extending strategies. Furthermore, even with a successful implementation of the Battery Regulation, the contribution of circularity strategies to increase supply and reduce demand is limited in the short term. It is therefore recommended to improve the prioritisation of lifetime extending strategies and to give extra focus to decreasing the material intensity of our economy.

The COVID-19 pandemic has changed travel behaviour and mobility in the Netherlands. It is questioned if the mobility system is subject to long-lasting change in the near and distant future. This study aims to observe the development of mobility impacts over time with a quantitative approach.

Current traditional transport models have difficulties implementing uncertainties such as the mobility disruptions of COVID-19. These models lack the exploratory nature to cope with quickly arising events and changes. Using system dynamics, a model is developed to observe the mobility impacts of disruption and trends in transportation. The developed model and alternative transport modelling approach are tested and validated by applying the case of the COVID-19 mobility disruptions in the Netherlands. By including a tele-activity alternative in the mode choice component of the model, an option not to travel is modelled as a contender to the traditional modes of transport using choice models.

Subsequently, applying the model to the COVID-19 situation provided insight into new relations and understandings of the development of the mobility impacts over time in the near and distant future. It was found that the future attractiveness of modes of transport changes as a result. Travellers' preference for private modes of transport will remain high in the forthcoming years, causing public transportation to recover slowly. It can be concluded that the system dynamics modelling approach is beneficial and has accomplished the design of a model widely applicable to COVID-19 and other mobility disruptions.

With the population growing and countries becoming more affluent, diets shift. These global dietary shifts contribute greatly to increasing greenhouse gas emissions because of their higher carbon and water intensities. This development is problematic considering both the growth in our global economy as well as our population. It is therefore crucial to find ways to reduce the impact of food systems. The mesopelagic zone in the ocean has recently been found to host a vaster source of biomass than expected, however, designing harvesting policies poses a deep uncertain issue. The mesopelagic zone promises a new source for the human food supply chain, especially serving as fish meal for aquaculture. Aquaculture is currently the worlds’ fastest growing food sector. However, unlocking the potential of the mesopelagic zone has challenges. The working of the mesopelagic zone and its ecosystem are still vastly unknown, as is the magnitude and variety of impacts harvesting might have on this. Moreover, the zone is located at a location beyond national legislation and still lacks overarching regulation. To acknowledge this research gap, this study focuses on how robust decision making methods can support decision making on harvesting in the mesopelagic system. This is done by analysing the current governance arena, the working of the mespelagic zone, and finally applying these findings to a System Dynamics model and performing Many-Objective Robust Decision Making (MORDM). Ultimately, to understand possible mesopelagic futures and thus gain governance relevant insights on harvesting in the mesopelagic zone. Exploring the model behaviour over a variability of plausible futures demonstrated that several uncertainties have a crucial impact on the mesopelagic system's best and worst case behaviour. It seems that profitability of mesopelagic harvesting relative to regular fishing is the most prominent indicator for collapses of the mesopelagic biomass. Additionally, factors concerning low food availability seem to be strongly linked to biomass collapse and vice versa. Moreover, reproductive parameters (e.g., a high female fraction of the biomass) as well as a high ratio of myctophidea to Maurolicus muerelli positively influenced the magnitude of food provision. Furthermore, other noteworthy findings are that an optimal harvesting quota seemed to act as a balancing tool for profitability. Additionally, exploration of the uncertainty space clarified which uncertainties have the strongest influence when no harvesting limits are in place. The two most influential uncertainties are the profitability of mesopelagic fish versus regular fish and the type of risk reward mechanism. This finding reduces the uncertainty space for decision makers by clarifying which indicators to focus on and which uncertainties might not even be relevant to consider for decision making. Moreover, it can be concluded that by harvesting according to the selected policies, the atmospheric carbon levels in 2100 would rise significantly enough to cause added pressure on the climate. This strengthens the call for considering climate change as well as biomass conservation when thinking about mesopelagic governance In conclusion, this research found several governance relevant insights for harvesting in the mesopelagic zone. The set of policies and the insights they provided serve as a promising starting point for stakeholders to learn about harvesting in the mesopelagic zone. Considering the mesopelagic decision arena, this study shows that current marine governance is unprepared to manage the magnitude and variety of the impacts harvesting can have on the mesopelagic biomass and its carbon sequestration function. Also, this research strengthens the call for including the mesopelagic zone and its role in the climate in future governance. These findings facilitate a discussion between stakeholders, which was previously impossible due to the deep uncertain character of the mesopelagic zone. This study is a first demonstration of the added value of MORDM for decision making on an intricate system like the mesopelagic zone.

In 2019, the Dutch nitrogen crisis emerged after the Dutch legislation on nitrogen pollution (PAS) was dismissed by the Dutch Raad van State. The crisis resulted in large scale demonstration by farmers and lingering uncertainty in the Dutch business sector. From an ecological perspective the current policy approach is limited as it focusses primarily on reducing the inflow of nitrogen to Natura 2000 areas. Widening the policy approach to better represent the ecological side of the nitrogen crisis provides alternative ecological solution avenues to resolve the crisis. To this end, a System Dynamics model is constructed that encompasses the nutrient cycles of N and P, as well as a level of ecological complexity. To investigate the model’s behaviour under circumstances of deep uncertainty an Exploratory Modelling and Analysis (EMA) approach is used. The results indicate a large potential for cost reduction and increase of ecological performance by aiming policy at ecological compensation instead of emission reduction. The implementation of ecological measures requires scientific substantiation to be accepted into the regulatory framework. The potential economic and societal benefits of an ecological policy approach should be a strong incentive for policy makers and scientists to develop the empirical knowledge for ecological engineering.

EPA: Multi-Modelling for Complex Challenges With the acknowledgement of international grand challenges, the demand for better and ever more complex models is likely to increase a manifold. Where single models may no longer be able to grasp the complexities of a system, multi-modelling (Nikolic, Warnier, et al., 2019; Vangheluwe, 2000) enables the combination of the strengths of different models whilst enabling the development of multi-perspective insights. In this research, I developed a multi-model to provide policymakers in The Netherlands with insights on how to steer the Urban Energy Transition at both national and municipal level. This is done by combining a System Dynamics (Forrester, 1961) model for evaluating market behaviour at the national scale, and an Agent-Based Model (Epstein & Axtell, 1996) for investigating investment behaviour at the neighbourhood level. The results from experiments with this model show that multi-perspective insights can be very helpful in designing policies and strategies for dealing with Sustainable Urban Development. It is found that challenges concerning the conceptualisation phase of the Model Development Cycle, could turn out to be opportunities for model development. As formalism selection and possible overlapping scales between models requires extra effort and attention, this attention results in model concepts that will be better evaluated, resulting in better concepts to be developed altogether. The main challenge that is currently unresolved, is that of the computational performance of multi-models. Although worsened by the choice for Agent-Based Modelling as one of the formalisms used in the multi-model, the computational requirements for the simulation seriously hamper the opportunities for experimentation and analysis. Although solutions can be found to minimise the computational demand or the effects of it, improvements are required in either computational demand or processing supply, for multi-models to become a suitable method for common modelling and simulation practice. CME: Energy Transition in the Built Environment With the EU goals (European Commission, 2018b) to reduce CO2 emissions to zero tons in 2050, The Netherlands has set up a climate agreement (Klimaatakkoord, 2019) in which municipalities get a leading role in reducing the carbon footprint of the Built Environment. To realise these goals, an energy transition is inevitable and strategies need to be developed for remodelling neighbourhoods into sustainable living environments. Before the end of 2021, municipalities have to come up with a plan in which they decide what neighbourhoods will be transformed and when. A problem in this is that currently, municipalities do not have the resources and tools to do so (RTL, 2019; Schuttenhelm, 2020; Straver, 2019). Currently, existing models, such as the Vesta MAIS model (Planbureau voor de Leefomgeving, 2019c) offered by the national government, are missing two crucial aspects which should help municipalities to set up their strategies: time dynamics and population dynamics. The techno-economic approach does present the ’best’ solution but is missing how to get there. Next to this, uncertainty around national policies makes that it is very difficult to decide on a robust policy. To deal with the aforementioned challenges, I use a multi-modelling approach (Nikolic, Warnier, et al., 2019; Vangheluwe, 2000), using System Dynamics (Forrester, 1961) and Agent-Based modelling (Epstein & Axtell, 1996) in which on the one hand, the time dynamics and population dynamics aspects are incorporated, and on the other hand multi-perspective insights on effects of national policies on national goals as well as insights on the heterogeneous effects at the municipal level are provided. An important finding is the crucial role of a CO2 tax on the realisation of national goals. Without this tax, homeowners and housing corporations will not be motivated sufficiently to significantly invest in insulation measures, making that fewer alternative heating systems will be implemented. Next to this, it is found that major differences can be distinguished between rural and urban areas. Heating grids will be a viable starting point for urban municipalities to start their transition due to the availability of residual heat. For rural municipalities, this will be more difficult due to a dependency on low-temperature systems. There, starting with the neighbourhoods that have the most potential to insulate is advised.

To provide insight into factors that play a role for DT in the humanitarian sector, a combination of literature and expert interviews was used to identify relevant criteria for the humanitarian sector. In accordance with prevailing literature, these factors have been categorised in people, process and technology factors. People factors include leadership, human resources, culture, organisational structure. Process factors were: alignment operations & IT, long term commitment Management, long term commitment network, long term commitment donor, crisis response, Legal issues. Finally, technology factors included both data and digital. The identified critical DT factors were translated into an assessment framework making use of the CMMi Maturity Model approach. For each factor and maturity level combination, there is an explanation of what a humanitarian organisation has to comply with, before advancing to the next level. This ensures that national societies can generate an overview where they currently are and what steps to take to advance. To adequately deal with the barriers and increase practical applicability of the framework, separate barrier-overcoming-strategies have been formulated.

The transition to a low-carbon economy depends on several critical materials, among which is cobalt. This metal is a main component in batteries, and therefore, demand for cobalt is expected to increase in the coming years. This paper explores future scenarios for the cobalt system, in light of its role in the low-carbon transition. The future of cobalt is characterized by deep uncertainty: it involves uncertainty on how the system is structured, on the values of variables influencing the system, and on the desirability of outcomes of interest. Therefore, this research uses Exploratory system dynamics modelling and analysis as the method, since it allows to explore an ensemble of models, each with different values for the input variables. The results show that cobalt is likely to remain a critical material for the electric vehicle transition, although the demand for the metal could vary greatly, depending on the size of future batteries and how fast the average oregrade of cobalt will decline. The demand scenarios in turn influence the development of industrial and artisanal mining, and recycling in the cobalt system. Depending on the collection rate of batteries and other cobalt-containing goods at the end of their life, recycled cobalt could account for a significant part of the demand. The by-product nature of cobalt makes future industrial mining dependent on developments in the nickel and copper markets. If the demand for cobalt increases sufficiently and its price levels go up, cobalt could become a co-product for nickel and copper. With regards to scarcity and sustainability of the cobalt system, this research examines the influence of the assumptions of the two opposing paradigms in the literature on these two concepts: the Fixed stock paradigm and the Opportunity cost paradigm. While the Fixed stock paradigm warns for eminent physical scarcity, the Opportunity cost paradigm places its trust in the regulating capabilities of the price of metals, the signal for economic scarcity. This paper argues that the continued discussion on the paradigms, divert the attention from the externalities of mining. Cobalt resources are not likely to soon be depleted, but externalities of mining could continue to increase. More cooperation between scientists of both paradigms could help to increase understanding of metal systems, and to produce better advice on policies for decision makers to cope with the externalities. Increased research into understanding how externalities of mining could be mitigated is therefore recommended.

These days, technological progress and automation are widespread, but productivity growth and thus overall economic growth lags behind. There is a strong conviction that the declining growth rates are caused by a shortfall in aggregate demand. This shortfall in aggregate demand might be due to the development of a ‘dual economy’. A dual economy is an economy in which a limited number of economic activities experience high productivity growth rates due to automation and technological progress (progressive sector), meanwhile the remaining economic activities experience almost no productivity growth because automation is barely present (stagnant sector). A dual economy develops due to unbalanced economic growth between the progressive and stagnant sector. To assure stable economic growth, it is important to slow down unbalanced growth. However, until today it is not known how to effectively target unbalanced growth, because the driving factors are not well known. This research tries to improve the understanding of unbalanced growth by finding the driving factors behind unbalanced growth. A macroeconomic modelling methodology is used to achieve this objective. Conclusions that provide information about which factors drive unbalanced growth, and how these factors should be influenced by policy makers to slow down unbalanced growth and stimulate stable growth are provided.

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.