Recognition justice is a form of justice that is achieved when people from different backgrounds can participate in society as peers, without having to assimilate to a dominant party. It is currently not being represented in climate change or conflict models.

This research aims to conceptualise recognition justice for use in agent-based models (ABMs), with focus on the context of resource scarcity. The conceptualisation was made using elements identified from literature. From these elements, recognition justice was defined as the absence of recognition injustices. To conceptualise it for modelling, these injustices were further specified as a series of individual misrecognition events that are caused by adversary actors. The events have a type and an intensity and they elicit an emotional response. Emotional responses to misrecognition events build up over time to form an emotional sentiment. This is a long-term disposition of an actor towards an adversary person or group. It is the main metric to represent recognition injustice, and it also influences emotional responses to individual events.

The conceptualisation was applied in an agent-based model representing the farmer-herder conflict over scarce natural resources in the Dosso region in Niger. The purpose of this model was to gain insight in how the conceptualisation acts when implemented. From the model behaviour it is concluded that the recognition justice conceptualisation does contribute to scientific research by offering a way to implement recognition justice in agent-based models.

The Dutch energy landscape is becoming more complex due to various interacting factors and uncertainties, creating what are known as "wicked problems" that demand collaborative solutions. This study addresses knowledge gaps by developing a collaborative guide for fostering effective Multi-Stakeholder Collaborations (MSCs) that can endure over time. Through literature reviews and interviews, 69 factors influencing MSC durability were identified and organized using innovative clustering techniques, resulting in the collaborative guide. To test the effectiveness of this guide and develop recommendations to an existing MSC, the guide is applied to the MMvIB project as a case study using a structured four-step approach known as the COOP method. This method involves Consultation, Observation, Organization, and Perpetual development phases. By prioritizing influential factors and adapting to project dynamics, this approach aims to enhance the long-term success of MSCs. The insights and methods derived from this research are relevant to a wide range of collaborative contexts. Future research should focus on refining the approach and developing implementation strategies for these factors.

The Dutch drinking water utilities are legally responsible for supplying drinking water to their assigned customer base. Continuing to supply drinking water for the long term is coming under pressure from challenges on the supply side, related to water quality and quantity on the one hand and developments due to increasing demand for drinking water on the other. Both developments are expected to be negatively influenced by the effects of climate change.
The Dutch drinking water companies face three major challenges regarding strategic investment decisions. First, the current sourcing and production capacity must be expanded to meet future drinking water demand. Second, there is a great demand for End-of-Life replacement of pipes in the drinking water infrastructure. Third, an investment challenge of a lesser financial magnitude but with an expected great impact on business operations is related to gaining operational control over the drinking water distribution network by integrating state-of-the-art sensor technology.

The outcomes of the internal decision-making processes of the drinking water utilities regarding these three strategic challenges will affect the stakeholders of the drinking water utilities. In addition, it offers possibilities for alignment with the goals of the other stakeholders. The main problem that this research seeks to address is a lack of engagement with drinking water utilities' stakeholders in the decision-making processes. A way to engage with stakeholders is by using Participatory Modelling, a technique that is not commonly applied by drinking water utilities.

These possibilities to engage stakeholders in the decision-making process are further backed by the development of new resources that have become available in recent years. These resources are new modelling techniques that have been applied in the field of drinking water research, in recent years. And, a novel perspective on multi-modelling e.g. the Multi-Model Ecology (MME) with Multi-Model Interface (MMI). In the current practice of research for Water Resource Management and other research for drinking water utilities, an MME and MMI (MME+I) have not yet materialised. This study aims to determine if an MME+I can benefit research for drinking water utilities and facilitate Participatory Modelling.

The Participatory Systems Design methodology (PSD methodology) is applied to generate a design for the conceptual model of the MME+I and the logical architecture for the MMI. A Proof of Concept (PoC) use case of model-coupling was applied. Here, an ABM model for Water Demand generates water demand patterns for an EPANET hydraulic model. This is a novel approach in hydraulic modelling for Dutch drinking water utility Oasen, since it introduces agents' behaviour from the ABM model to the modelling of hydraulic networks. It demonstrated that the outcomes of an ABM model affect the performance of the EPANET hydraulic model. In addition, It provided insight into how changes in water demand from scenario studies can affect strategic investment decisions for drinking water utilities.

The energy transition is a pressing and widely debated topic in society and is creating global challenges. As the switch is made to sustainable energy carriers, more pressure is being put on to our electricity grids and the network operators managing them. They, however, face the uncertain future capacity needs of the grid and operate in a complex socio-technical system. The energy transition will require many collaborations and interactions which suggests a new decision-making process to facilitate the energy transition. An insight into the current decision-making processes of these network operators is however still lacking in literature and remains an open question. Therefore, in this thesis, I develop a conceptual model of the decision-making process by network operators for long term decisions under uncertain conditions. To achieve this aim, I first collected data about the decision-making system using semi-structured interviews of decision makers in the Dutch energy network. I then investigated models of decision-making in policy making, economic theory, and psychology literature and eventually chose the recognition primed decision-making (RPDM) model as fitting the most requirements. The model is built step by step and adapted to describe the analyzed decision-making system. The model is ultimately conceptualized in three layers: interactions between decision makers, individual decision-making, and changing problem perception. Significantly, the model is adapted to include the concept of a problem perception. The model has some limitations, as the face validation was not included in the modelling process and several mechanisms remain unspecified, such as the interactions between decision makers. Two decision situations that network operators themselves described facing in their current decision-making were used to run the model qualitatively. Given the limitations, the model was still able to provide insight into the decision-making of network operators. The model describes that decision makers respond to uncertainty by looking for more information, posing question to what extent reflection on the problem perception are also used to cope with uncertainty. The problem perception influences the model substantially, suggesting that a change in perception would make decisions such as the dilemma of prioritizing clients more adequate. Further implementation of the model is recommended to provide insight into the decision-making process to help facilitate the energy transition.

This research has illustrated the abilities of incorporating robust decision making within train maintenance. Both scheduled and unscheduled maintenance processes of the Nederlandse Spoorwegen (NS) have been examined, after which exploratory modeling and analysis enabled developing valuable insights for decision makers. Scenario analysis through simulation allowed for the quantification of the effect of deep uncertainty in relation to train maintenance. From the scenario analysis it is discovered what uncertainties are, according to the simulation model, of significant influence towards maintenance performance. The results presented allow decision makers to support their decisions when evaluating which type of policies to perform within the next decade. While the outcomes are specific for NS, the aim is to show that the approach held in this research is also applicable throughout other fields where decision making information quality should be improved.

Due to climate change, there is a push toward a reduction of greenhouse gasses. Through spatial planning, the province of South Holland plays a role in this process. However, this requires insights into relation between the energy system and the built environment that is currently lacking. This thesis shows how spatial clustering using the K-prototype algorithm can be used to find areas with similar characteristics. It also describes how this information can be used in spatial planning by decisionmakers like the province of South Holland.

Serious Games can be used for many purposes, including studying complex, socio-technical systems. They are claimed to provide new opportunities by bringing different knowledge and fields of expertise to the table. Combined with simulations that allow for observation of the evolving infrastructure under deeply uncertain conditions, they have the potential to be an effective research tool. An example of such a serious game is Infrarium - an adaptive cyber-physical game that addresses the challenge of collective decision-making toward emission neutrality in the energy transition process within the port. The enormous space of paths that players could follow and the complexity of interactions available raise the question of whether it is possible to create such an intervention method that would allow for steering the gameflow. This research addresses this question by introducing the concept of a Network of Puzzles based on which the Infrarium model is built. After concept evolution was done while searching for a suitable tool, the System Modelling Language (SysML) was chosen. The game was successfully represented in the conceptual model that captures the system's complexity sufficiently enough to provide the coherent structure of the game within which the clarity and traceability of connections provide a design space for future development as a tool for shaping the game flow. The results are presented in the form of SysML diagrams followed by a discussion about SysML as a supporting tool in model-driven game design, within which the possibility of further vertical elaboration with requirements and limitations is justified. We have proven that further development of the game elements should not jeopardize the existing functional level of the model. The SysML model in the presented form could be used to design an intervention method to shape players' experience during gameplay.

The Dutch government described its vision to achieve significant reductions in greenhouse emissions in the climate goals of 2030 and 2050. The energy infrastructure in the Netherlands will be a critical factor in achieving these goals. As an extension to the stated importance, understanding the current energy infrastructure is equally significant. Models often form the base of understanding such complex systems. Unfortunately, the modelling environment of the Dutch energy infrastructure is fragmented. There exists no one model that is able to give comprehensive oversight in order to provide policy makers with the information needed to facilitate key energy policy decisions. However, there is a variety of models present that each clarify their own piece of the puzzle. Therefore it is the ambition of the TU Delft and partners to create a multi-model infrastructure that is able to couple existing energy models in order to facilitate comprehensive energy policy creation. This thesis is part of the research needed in order to achieve this higher goal.

The main conclusion of the conducted research is that the problem described in the main research question can be answered by using a coupling process based on audits, comprised of questions aimed at detecting issues and checking the effectivity of the means to solve the issues. These audits have proven successful in completing two different multi-resolution multi-modelling coupling case studies. The process entails (for a two-model coupling) a separate model audit for each model, leading to a coupling audit using both models and finally to the realisation of the coupling itself. Adherance to the process standardises the way couplings are created to a degree. Because of this, model audits done for one coupling could for example be re-used at a later date for another one. This provides value over old coupling methods, which were often done individually in an ad-hoc manner.

The effect of using decision support systems in decision-making processes characterized by time pressure, uncertainty, and dynamism is still very limited and debated among researchers. By utilizing a case study that concerns the employment of a DSS to assist the procedures of intercepting fugitives undertaken by the Dutch National Police, this thesis investigates the difference in the personal efficiency and productivity of the practitioners (the decision time) between the current practices and the model-supported ones. An agent-based model is thus implemented which mirrors the procedures of intercepting fugitives in the two situations. The results highlight that, in contexts of time pressure, uncertainty, and dynamism, the use of the DSS does not always result in faster decision-making processes. This highly depends on how the DSS is implemented, especially in relation to the level of experience of the practitioners with the activities at stake and the level of dynamism and complexity of the decision-making process. Moreover, it is important to guarantee flexibility in the design of the system, and provide the users with the most relevant information to make the decision, so that the time needed to gather and process the information is minimized, while still guaranteeing an appropriate level of accuracy of the information displayed. Finally, the results show that involving the practitioners in the design of the system can improve the decision-making time, since it reduces the discussion between the actors, but is less relevant than the time needed to gather and process the information (which is a very critical aspect in contexts of time pressure), especially for those who work in contact with the system.

Over the past decade, renewable power generation through wind power has increased significantly. Especially the installation of offshore wind energy is expected to skyrocket to a total of 2000 GW by 2050. The majority of rights to develop and operate offshore wind farms are allocated through tendering procedures.
In tender procedures, interested parties issue a bid, which includes a subsidy price called strike price at which the party would be willing to accept to develop and operate the wind farm. Lower strike prices increase the chance of winning the tender. In principle, this should lead to the cost-effective deployment of wind energy. However, current wind farm tender designs have been found to cause multiple undesirable results. Understanding the behaviour of the bidding parties that leads to these flaws will help policy makers improve the design of offshore
wind farm tenders to counteract this.
Current modelling methods have failed to incorporate the influence of bounded rationality in the bidding procedure together with a realistic representation of the economic valuation of the tenders, which all bidding organisations conduct. This knowledge gap is addressed in this thesis. This thesis has performed a careful sensitivity analysis on a valuation model provided by the industry to find its influential parameters, developed a coupled model by designing an agent based model and connecting it to an economic valuation model, and conducted exploratory experimentation with the coupled model.
The work presented in this thesis is a proof of concept for using coupled modelling for socio-economic processes with a strategic component. The work contributed to the usage of model coupling in cross-disciplinary studies and encourages the research community to use coupled modelling for socio-economic processes in other domains as well.

The world is currently a rushed environment; things change faster than ever. Technological innovations impress us again every year. Many innovations that enhance society are the result of innovating organisations. These organisations invest in innovative solutions, for their own good, but eventually also for the greater good. Allocating resources to these investments is therefor an important aspect of innovation. This thesis has been conducted to gain more insights in the resource allocation process, the process which determines the investments executed in organisations. The resource allocation process model as defined by Bower and Gilbert helps managers and executives to create awareness about allocating their valuable, finite resources. Bower and Gilbert tried to create a simple model without a lot of predefined mechanisms, but their model lacks detail and therefore the inner mechanisms of each sub-process of the resource allocation process is a gap in the literature, there are some scholars who describe specific cases in detail, but there is no generic interpretation. This research gap has been the foundation of this research and has led to the development of an agent-based model of the resource allocation process. Within the development of the simulation model all core processes had to be defined in detail. And since the objective is to develop a generic simulation model, the core of the sub-processes and inner mechanisms had to be defined. One of the most important mechanisms is the way an organisation can influence their own resource allocation process. This happens through structural and strategic context determination. With these two types of context determination, organisations can influence their allocation process in order to either execute more induced strategic action (action which corresponds with the defined corporate strategy) or more autonomous strategic action (action which lies outside the defined corporate strategy). Which type of strategic action is necessary depends on the specific situation of an organisation. With the knowledge of these two kinds of determination the sub-processes and their mechanisms had to be defined. Definition of these sub-processes happened through literature research. There are two topics within these processes: Definition and Selection. The basic principles of these two topics had to be known in order to be able to formulate a possible generic detailed definition of the sub-processes. This resulted in a conceptual model which, through formalisation could be implemented. Before implementing an agent-based model the detailed definition had to be complete in order for the model to function. The implemented model has been tested on known phenomena derived from literature. The model is able to reproduce the expected strategic action under specific organisational context settings. Within a dynamic environment the model reproduces expectations for the contextual settings. And the mechanism of an increasing opportunity space can be reproduced. The model is unable to fully capture the effect of communication and divergence on the autonomous strategic actions. These results support concluding that the developed agent-based model is able to simulate the resource allocation process of a hypothetical organisation. With the model, the boundaries of the resource allocation process can be explored on proportion of control versus organisational learning. The model is also be able to analyse the effect of organisational changes on the strategic actions. Currently the model as implemented is unable to simulate a real-world case, but the potential for use within organisations and further research is certainly there.

To deal with increasing complexity and connectivity of socio-technical systems it becomes unlikely for individuals to be able to oversee all possible changes. These systems are riddled with a plurality of actors with differing interests, disciplines, institutions and ecological limitations. Examples of systems like these are energy and gas grids. If one wants to tackle problems on these systems one would ideally understand possible results of changing things in these systems as a change in one part of the system can lead to results in other subsystems. If a tree falls down on an energy pole, for example, chemical plants can stop functioning. This in turn can cause orders to be late, influencing a whole production chain.
To this end one would ideally one would ideally apply systems thinking: ”Systems thinking is a set of synergistic analytic skills used to improve the capability of identifying and understanding systems, predicting their behaviors, and devising modifications to them in order to produce desired effects. These skills work together as a system.”
However, because the earlier pluralities it unlikely for a single individual or organisation to have all the required information for this. Processes are required where information is collected and co-created with multiple parties in such as system to enable the creation of comprehensive solutions for these problems. What is required is social learning. Social learning (SL)is learning that happens by people participating in so called communities of practice. A community of practice can be seen as a group of people with converging interests and skills. An example would be a grid operator, which have their own sub-communities (E.G. cable technician or systems manager). They can be smaller groups, E.G. a family, and participation is often not mutually exclusive. Rather than being part of a community of practice one could be seen as being part of the landscape of practice, consisting out of multiple communities. By partaking in these communities people gain experiences by both learning and expanding on a communities’ knowledge.
The aim of this thesis is to gain insights into: 1. mechanics shaping and steering social learning 2. how to measure social learning processes for in vivo experimentation 3. design mechanics for participatory modelling processes and Social learning in general to improve development of such processes 
To do this a theory has been made on the mechanics and behaviour of individuals in a SL process. This has been done in chapters 4, 5 and 6. To do this a PM perspective has been used as this give a structure on the actions someone can take and requires shared information to be structured. Additionally it is seen as a useful tool for tackling socio-technical problems.
The theory thus focussed on the main action of PM, namely sending information, receiving and processing information and deciding upon the model. To develop the theory knowledge from multiple disciplines is needed. To this end a theory has been developed using supersynthesis. This is a research method where multiple theories are combined to make a new on to explain something. The aim is not to supersed the combined theories, but to explain something new. The fields that have been the focus are communication science, helping understand how information is processed and sent, and social psychology, helping understand why and when individuals take certain actions. The theories have been synthesised in two rounds of conceptualisation, with the first focussing on conceptualising every possible action and mechanic. The ones that were deemed most interesting or useful have been conceptualised more in depth. To this an additional conceptualisation of knowledge and information is made. The final theory is as follows:
Knowledge takes the shape of a knowledge graph. In this graph fields of expertise are called topics. One can think about the weather as such a topic. These topics consist out of information items, think cloudiness or temperature, and links between these items indicating their relation, think cloudiness leads to lower temperatures. These items may have links to items from other topics. For example cloudiness is related to sun hours and yields of Photovoltaics from th topic of Photovoltaics.
It is assumed that a complete knowledge graph exists. Each individual knows part of this graph, signifying knowledge or expertise in the topic. The larger part of a topic they know the more expertise they have in that topic. This includes both information items as their links (relationships). All these items, links and topics also have a perceived relevance for people. This is based on interests or affiliation. Affiliation means relationship with a group of people, for example, meteorologists. Something is also seen as more relevant if it is discussed often, attributed to common knowledge effects. In a social learning process there are several individuals. Each round they are able to share information. Information is seen as information items and/or their links. A topic as a whole can also be discussed, but this is not seen as actual information for learning. What they share is based on the amount of energy they have and are willing to spend on sharing information. This is dependent on perceived relevance of the item they are considering to share, their expertise on the related topic and their tiredness. It is assumed that the energy one has decays over rounds.
Shared energy is received by others and they start to process it. Here something is integrated or learned if they are able or willing to invest enough energy in the processing. This is dependent on their expertise of the related topic, their perceived relevance of what is shared and their attitude with regards to the sender. If expertise, relevance and attitudes are high enough someone will process and integrate information. Processing energy is also assumed to decay each round.
In addition to information on knowledge, individuals can also share relational information. These are details like hobbies and other personal details. These are processed as either positive or negative and influence attitudes.
Processing of shared information may also happen during breaks or downtime. Here one has more energy to spend and attitudes are less relevant. Total recall of information is assumed in the whole model (I.E. people do not forget anything).
To allow for further reflection on the theory and to act as a proof of concept of the theory the theory has been translated into an agent based simulation model. This model has been analysed in a sensitivity experiment using LHS and extremely randomised forest in addition to a variety of plotting techniques in R. 
Additionally two experiments are designed, inspired by real cases. These are used to reflect on the theory and find less noticeable quirks from the ABM. 
Based on the theory and ABM the following things have been concluded:  -While communication science theories and social psychology theories have been used for theory development, they are not a be all end all. One can apply other fields if one wants. This specific combination, however works especially well for an individual perspective.-The theory can be used to reflect on SL by practitioners as it tells why and how people can act. Furthermore the idea of the knowledge graph can be connected to landscapes of practice, with topics relating to a community and links between items of these topics to those of other relating places where boundaries interact -Matters like conflict and increasing conflict, coalitions, personal inhibitions and norms are some of the values that would make sense to include in the theory. This would make the theory less usable for simulation modelling, however and would add a lot of behaviour that is not directly related to the learning process. To implement these additional behaviour could be conceptualised and added, making the theory more complete but less comprehensive. The most important addition that could be made according to me would be an extension on the actions influencing attitudes and the actual definition of a process result (a participatory built model or a plan). -This new theory is valuable as this individual based perspective has not been taken before, inviting to reflection on practice.-The knowledge graph could be used as a means for building new theories that are comparable. Additionally it is a way to explicitly learning. -The combination between social learning and Participatory modelling has not been made this explicit before. It would allow participatory modellers to reflect on their practice.-The ideas of energy for sharing and processing are quite influential in the ABM. They are interesting as they give clear reasons why learning may not happen or happen suboptimally. For learning to happen information need to be shared. If people lack the energy or the willingness to spend energy sharing will not happen. If they do not have the energy to process this they will also fail to learn. This highlights the need for keeping energy levels in mind when designing these processes. It is assumed that these energy levels decay linearly. While arguably too simplistic still it does show how intensive processes or boring processes can fail.
The following design mechanics are proposed:1. Usage of a knowledge graph to keep track of what is learned by researchers 2. Usage of knowledge graph to steer the process order that makes learning more likely (topics that closely relate to all participants first and expand that towards specific participants later down the line). 3. Use set structures, conceptual modelling, drawings and other tools to make information sharing and processing easier and less intensive. This would make the process spend less energy if the used tools are chosen well (I.E. a conceptual drawing of what is said or what someone wants to explain using causal diagramming is probably better to explain ideas than doing so via live programming of a simulation model). 4. Use actions like summarizing what has been said to slow down the process if it becomes to quick, leading to a processing energy deficit. 5. Use means like using an agenda to ensure the speed of the process does not become to slow, leading to boredom and potential energy decays

In order to reach the climate goals agreed on in Paris, immense CO2-reductions is necessary: 55% reduction in 2030 and 95% in 2050. 20% of the total CO2-emissions in the Netherlands comes from heating homes in the built environment. Therefore, it is essential to transition to a sustainable heating system in the built environment. According to the dutch climate agreement of 2019, municipalities are given a leading role in this transition. Municipalities became responsible for setting out the strategy for this transition. They decide for each neighborhood within their jurisdiction how the reduction of heat usage through insulation can be realised. Furthermore, a switch to a sustainable alternative for natural gas needs to be transitioned to. Consisting these two steps, this strategy has to be formalised before the end of 2021 in the `Transitievisie Warmte' (TVW).
However, decentralised coordination of this transition has its limitations: municipalities lack the knowledge to oversee the consequences of their decisions on this complex socio-technical system. Decentralising responsibility means increased demand for knowledge. This lack of knowledge from municipalities lead to decisions made without understanding the consequences of their local decisions on the regional and national electricity and gas networks.
These networks can be limiting for the decision freedom of municipalities and their transition plans to a sustainable heating system. Because they share the same energy networks, municipalities’ choices can limit the options of neighbouring municipalities. The objective of this research is to increase the understanding of the consequences of local decisions in the heat transition. This will be done by creating a model which uses scientific knowledge to support policy making.

Migrants are moving in great numbers towards urban areas as a result of urbanization and drivers such as political-, economical- and climate crises. The influx of new people brings new challenges for cities and municipalities to provide for a suitable environment for new and existing citizens whilst preventing adverse effects to occur. Current literature describes the dynamics of the city using effects such as segregation, gentrification and urban decay. However, the changing character of urban areas is not linked with the decision-making behavior of citizens in current literature. Furthermore, the dynamics of urban areas are rarely observed from a meso-scale perspective. This research aims to describe the relation between migration patterns and changes to the fabric of the city. A conceptual model on the dynamics involved in observing decision-making of citizens and observing changes to the city fabric is made. Using real-world data of the city of The Hague in The Netherlands, a Data-Driven Agent-Based model is made in Netlogo. Data analysis on the outcomes of the model using Python provide a better understanding of the impact of migration on the changes to the city fabric. The model output describes the impact on the city caused by the influx of migrants. More migrants entering the city can drastically increase homelessness or the "pushing out" of citizen groups because of housing shortages. This is most prevalent for the poorest citizens, since social rent housing is first to become scarce when more people enter the city. An increase in income inequality or a decrease in average migrant income results in even more pronounced housing shortages. The model simulates 6 policy interventions for the municipality of The Hague. The model shows that all policies that increase housing options significantly increases well-being of citizens. However, the policies that try to improve safety and health do not show an increase in well-being of citizens. By means of historical validation the model is tested for validity. The model is able to describe much of the decision-making behavior of migrants and citizens. However, for certain ethnicities, the model is currently unable to describe the decision-making process in such a way that it can represent the observed real-world moving patterns. A framework has been presented that can be used to better explain the impact of migration on cities. Furthermore, the conceptual model can be applied to different aspects of city dynamics, for instance the transport or energy sector. The ABM shows that current housing options and expected influx of migrants become problematic within the coming 10 years. An indication of the usefulness of proposed policy interventions is presented. More research and surveys are needed to better understand these mechanics and their relation to changes to the fabric of the city. Additional research into the changes to the neighborhood amenities and businesses as a result of changes in citizen composition can aid policy making and understanding the developments of the ever-changing city landscape.

In the past few decades, we have witnessed unprecedented impacts of climate change. The increase in Green house gas emissions due to human activities has disastrous implications for earth including an increase in global mean temperatures, rise in sea level and melting of polar ice caps. Climate change has
impacted all forms of human life on earth and if unchecked, poses a threat to human existence. With more than 50% of global population currently living in the cities and the upward trend of people migrating to the cities expected to increase in the next few decades, cities are one of the major contributors to climate change. Nearly 80% of global energy and 75% of global resources are consumed in cities. Thus, there is an urgent need to tackle the environmental impacts of cities.
In this research, we develop a methodology to quantify and analyze the environmental impacts of cities by considering the consumption of all resources
occurring in a city. The methodology is applied to the city of the Hague in the Netherlands but can be replicated for other cities as well. The research is divided into the following components: Firstly, a small literature review is conducted to identify different elements in a urban system. The literature review also assesses different models used to quantify environmental impacts of a city . Out
of the three models reviewed in the study, Life Cycle Assessment (LCA) is found to be the best fit for assessing environmental impacts of city. Knowledge gaps surrounding the applicability of LCA to a city are identified and based on the knowledge gap, research question is framed. Following the literature
review, a top down approach is used to identify products or activities by residents of a city that have an environmental impact. Following that, a data disaggregation methodology is developed to downscale data related to resource consumption and activities from the national or European level to local level of
neighbourhoods in a city. The disaggregated data is then quantified using LCA and analyzed for different geographical regions, different resource use categories and different demographic groups in a city. Finally, based on the environmental impacts, commonly implemented policies in cities around the world to reduce GHG emissions are modelled and analyzed for the case study : The Hague. This is followed by detailed discussion on results, limitations, conclusions and directions for further research. The main conclusions that can be drawn from the research are that resource use categories in which
intervention by cities is possilble account for nearly 70% of Global warming potential (GWP): 45% mobility, 15% waste,10% energy. Larger households have a higher impact due to mobility whereas smaller households have higher impact due to energy. Environmental impacts are further analyzed for clusters of
neighbourhoods based on their socioeconomic indicators. Finally, the policy interventions analyzed show a potential to reduce net GWP by 25% in the Hague

The concept of a hydrogen economy has gain renewed attention as a way to enable a cost-effective transition to a low-carbon society. The adoption of hydrogen as an energy carrier beyond its current non-energetic use is however faced with deep uncertainty, making decision-making on policy support difficult. The state-owned enterprise (SOE) is evaluated as a particular type of policy support that may be implemented. SOEs are generally uniquely positioned in the energy system, due to their connection to policy-makers, their access to capital and their ownership of existing assets. An agent-based model (ABM) is created to evaluate the effects of SOEs on the emergence of a sustainable hydrogen economy. The model is experimented with using an exploratory modelling and analysis (EMA) approach. The results show that the SOE can strongly reduce the carbon intensity of hydrogen production, depending on the specific mandate used. A national carbon tax can achieve this effect as well, but the SOE has the added benefit of providing capital to asset investments, stimulating knowledge sharing and enabling investment in riskier technologies. This last benefit is especially important if the SOE is used to enable early deployment of electrolyser technologies. Further measures would have to be taken to facilitate this deployment, because in the coming decades electrolysers will remain far from cost-competitive under most market conditions.

The residential built environment in the Netherlands is on the verge of a transition of its thermal system. The Climate Agreement has suggested that municipalities will have to play a leading role in transitioning their neighborhoods' currently natural gas-driven heating systems towards sustainable alternatives. But changing the thermal systems of these neighborhoods will have consequences for the gas transmission system operator: Gasunie. Currently, no model captures this transition from the perspective of municipalities as key instigators while studying its implications on a transmission system operator. Therefore, this thesis presents a data-driven agent-based model representing the Dutch built environment consisting of municipalities, neighborhoods and energy sources to study the effect of municipal decisions in this transition on Gasunie's infrastructure. A novel municipal decision-making framework is designed that hypothesizes various factors contributing to municipal decisions in this transition. Aggregating peak gas demand to the level of Metering and Regulation stations in Gasunie's network captures the emergent behavior of interest from an infrastructural point of view. Simulation results show that the decisions of municipalities matter for the regionally required capacity in Gasunie's infrastructure, depending on the regional built environment characteristics, national transition goal, unfolding scenario, and municipal strategies. But the limited availability of renewable gas is a significant model driver and limits municipalities in what they can decide. It can be concluded that insight in the supply-side availability of the renewable gasses for the built environment in combination with multi-stakeholder coordination with the national policymaker and municipalities on the thermal transition can contribute to unraveling the uncertain character of this system and its implications for Gasunie's infrastructure. The agent-based model can expose the formulated theory of municipal decision-making and its effect on Gasunie's infrastructure while exploring and discovering what-if scenarios of interest for Gasunie and other stakeholders. It can potentially be used as a decision-support or debate starting tool by the involved stakeholders to show how their interests, characteristics, and decisions shape the change of this thermal system towards its sustainable future.

Increasing ship emissions are of big concern because they contribute to the effects of climate change and have an impact on the local and regional environment. Due to these concerns, stricter regulations are enforced upon the shipping sector by the International Maritime Organisation and the European Union. However, since new regulations are enforced, stakeholders have been slow to react. A key reason why investment decisions are not taking place is that of uncertainty in regulations and policy. Besides, the availability of bunker infrastructure in ports is key to the development of alternative fuels. The objective of this study is to obtain insight into what possible future scenarios of the deployment of alternative fuels for the short sea shipping might arise and to provide insight into the effects of collaborative port strategies on the emergence of alternative fuels European maritime short sea sector taking into account regulatory and technological uncertainties. By means of an agent-based modelling approach and exploratory modelling and analysis approach, the influence of these uncertainties and policies is addressed. This enables to obtain a better understanding of where the system might go. The findings of this study show the effect of regulation enforcement and the need for a well-developed methanol bunker infrastructure across Europe to enable the transition to alternative fuels.

The need for an energy transition has arisen in response to a growing discrepancy between (A) what people find important or valuable in life, and (B) the performance of – or value delivered by – the physical energy system. Since the physical energy system has not changed much in recent decades, it is clear that changes in people’s values are responsible for the currently observed mismatch between A and B. It is relevant to investigate how – and/or whether – this divergence can be managed as levels of societal discontent rise in proportion to the incongruity between A and B.

Minimizing the divergence of A relative to B (and vice versa) calls for a deeper understanding of how belief system change happens. Anticipating belief system change may help to forestall the emergence of negative sentiments and consequent destabilization of socio-technical (energy) systems. One way in which belief system change may destabilize the functioning of the Dutch energy system is through altering patterns of consumer energy technology adoption behaviour.

A person’s values interact with her knowledge to determine the attitudes he/she
holds with respect to clean (i.e. low-carbon) energy technologies. These attitudes, in turn, impact one’s intention – and ultimately one’s decision – to adopt a clean energy technology. Hence, value and knowledge change induces attitude shifts which subsequently affect consumer intentions to adopt clean energy technologies. The dynamic interplay of values, knowledge, and attitudes is referred to as belief system change.

This thesis focusses on providing a way of thinking about how belief system change happens within an energy-system’s context. Additionally, this thesis investigates the usefulness of ABMs as research tools for systematically studying belief system change within socio-technical systems (energy systems in particular).

This thesis researches the influence of various communication channels, considering the psychological aspect of consumers, to the adoption process of residential PV system, using agent-based modeling case study in the Netherlands. Agent-based model is used to explore how PV adoption dynamic behavior emerges as the result of information transfer between individuals on a new technology. Diffusion of innovation theory is used as the basic framework on how the usage of new technology can spread and theory of planned behavior is utilized to get better understanding on consumer's psychological decision making process. Further discussion with a representative of power utility and interviews with 2 PV installers are done to obtain more knowledge on the system. The study shows that mass media has important role in spreading information to speed-up the adoption, more importantly in providing initial knowledge to households that have not had interest on PV yet. The role of information is further shown as there is indication that households' interest tend to increase with more knowledge available on the internet, and at the same time, leads to the increase of adoption rate. A crucial role of communication channel is to draw PV installer together with potential adopter. Finally, the adoption rate is strongly slowed down by household's basic reluctance on new technology and lower income level.