Traditional financial companies play a central role in ensuring financial stability and reliability. However, these companies face institutional problems due to inefficiencies, lack of transparency and limited innovation. For this reason, the use of distributed ledger technology, or blockchain, has been explored as an alternative and its integration into traditional finance towards a 'hybrid' approach. The Dutch mortgage market was chosen as the focus of this research and is further analyzed due to its high transaction costs, including the high cost of trust. This led to the research objective of designing a Hybrid Mortgage Lending System (HMLS) that reduces the cost of trust.

A design science research methodology was used for this research where two interwoven artefacts were developed. The first artefact was a contextualized trust model based on three trustworthiness factors: integrity, ability and benevolence. The second artefact was a demonstration for the HMLS using a technological model.

The trust model was validated through expert interviews and enriched with additional information. Based on the feedback from the expert interviews, nine meta-requirements for the technological model were developed. The proposed solution for the design of the HMLS is to automatically distribute fractional ownership of mortgages via Non-Fungible Tokens (NFTs) through an ecosystem with the traditional finance company as originator and public investors as lenders. The ownership of the NFTs is transparently tracked via the blockchain to monitor the fractional ownership of a home. These NFTs exist in mixed token pools and are controlled by the traditional finance companies. These pools are supplemented with other digital assets to create different risk profiles to suit different types of investors. A demonstration of these token pools is shown, with an overview of the architecture explaining the mortgage application and transaction processes through the different layers. Finally, a stakeholder map is shown and their roles within the ecosystem are explained. The research provided new insights by making the second model dependent on the first. The findings highlight new challenges for further research, such as prototyping or improving the versatility of the model.

In recent years, the introduction of shared micromobility options such as scooters, mopeds, and bikes has transformed transportation in densely populated urban areas. Interestingly, when looking at shared moped availability, it can be observed that they are rarely available in less densely populated areas, such as the suburbs around large cities. This can be explained by the fact that financial viability for private operators is unlikely in these regions because of the lower demand compared to highly densely populated areas. Nevertheless, the introduction of shared mopeds to these suburban regions comes with many external societal effects. These effects can not be internalised by a private operator but could be internalised by a governmental body. However, from a governmental perspective, allowing and supporting shared mopeds in their region is only interesting if these external effects result in a societal welfare increase.

To this end, a societal cost-benefit analysis (SCBA) has been conducted to evaluate and quantify the societal effects of introducing shared mopeds in the suburban region of Schiedam. Using this analysis, an overview of the net societal benefit of shared mopeds and the profit/loss for an operator in a suburban region has been obtained. Seven conceptualised effects were valued quantitatively; operator setup cost, operator operating costs, operator revenue, accessibility, carbon emissions, noise emissions, and traffic safety. Three conceptualised effects were valued qualitatively; parking nuisance, public space freed, and the option value. Furthermore, two shared moped operator business models have been investigated; free-floating and hub-based models. To show the uncertainty of the net societal benefit figure a sensitivity analysis has been performed from the perspective of an operator. This sensitivity analysis has three scenarios: worst, nominal, and best.

Accessibility and safety are the most important positive and negative externalities, being responsible for over 80% of the net societal benefit in all scenarios. The SCBA net societal benefit is positive, for the nominal scenario when an operator uses a free-floating model. The net societal benefit becomes negative when using a hub-based model. The difference in the net societal benefit value between the worst (very negative) and best (very positive) is large. This shows that there is a high amount of uncertainty in the SCBA result, which is caused by not all effects being included and safety and accessibility having a considerable influence on the overall societal benefit figure. Nevertheless, this research provides a framework to investigate shared moped operator viability and the societal impact in a (potential) region. Moreover, it gives insight into that safety and accessibility effects should be prioritised when discussing the introduction of shared mopeds into a suburban region, but also which effects still need to be investigated further.

Renewable energy projects on land in the Netherlands face delays due to social resistance, driven by concerns over aesthetics, environment, and economics. Local energy communities have been identified as potential solutions to enhance social acceptance and overcome these barriers. The Dutch Climate Agreement targets 50% local ownership in onshore solar and wind projects to enhance public acceptance and project feasibility. Existing research highlights economic, institutional, and organizational barriers, yet limited research has been done on the internal governance structures of local energy communities. Moreover, no prior studies have explored the discrepancies between formal regulatory frameworks influencing design decisions and their practical application in Dutch local energy projects. This study employs Williamson’s four-layer model and Elinor Ostrom’s IAD framework to explore institutional design options for local energy communities in the Netherlands. Key findings reveal that European directives and Dutch laws support energy communities without specifying legal structures, allowing flexibility in governance structures. Practical implementations of institutional design options vary based on project needs and dynamics. However, most local energy communities adopt cooperative models and often establish separate entities like private limited companies to manage project associated risks. Based on the findings it is recommendated to organize local energy communities as cooperatives, addressing knowledge and resource gaps through partnerships with experienced entities, and establishing separate project entities to ensure local ownership and mitigate financial risks. This study provides practical insights for stakeholders navigating the complexities of local energy community projects.

The latest state-of-the-art large language models (LLMs) are implicit computational models of humans due to how they are trained and designed. This implies that LLMs can be used as participants in economic and behavioural experiments. However, their technical and ethical limitations have sparked ongoing debate about the usefulness of LLMs in experimental research. This study contributes to the research area by reproducing the “Flip a Coin or Vote” experiment conducted by Hoffmann & Renes (2021), with GPT-3.5 and GPT-4o as participants.

The findings indicate that GPT-3.5 (1) struggles to fully understand the rules of the experiment, especially in calculating payoffs; (2) has difficulties interpreting negative and positive valuations; (3) fails to match the percentage of rational choices observed in the lab results; and (4) demonstrate results that deviate too much with the lab results to be considered human-like.

In contrast, GPT-4o shows greater promise as a ‘good’ participant. GPT-4o understands the rules of the game correctly and demonstrates a much stronger ability to make rational choices, even matching, and sometimes surpassing, the percentage of rational choices made by human participants. However, in the second part of the experiment, GPT-4o's decision-making becomes "inhumanly" accurate. Moreover the results deviate too much with the lab results to be considered human-like.

Future research should be focused on how to steer the results of LLMs towards more human-like results, possibly through enhanced prompting techniques or further model training and fine-tuning. Furthermore, future research should focus on establishing best practices for developing a more standardised approach to conducting simulation research with LLMs.

This thesis addresses the critical issue of advancing the transition to renewable energy by focusing on onshore wind park development. In countries like the Netherlands, where population density is high, wind energy projects often face resistance from local communities. Local Energy Cooperatives (LECs) have emerged as a potential solution, offering a way to involve citizens more directly in the ownership and development of energy resources. This approach is seen as a way to reduce opposition and enhance acceptance of wind energy projects. The core aim of the thesis is to explore how structuring local participation through LECs can contribute to accelerating the energy transition.

To investigate this, the research employs an institutional case study approach, drawing on Williamson’s New Institutional Economics Theory and Ostrom’s Institutional Grammar. These frameworks are used to analyse both formal institution, such as laws and regulations, and informal ones, such as norms and shared values that influence the development of wind parks. By examining three specific cases, the study assesses how communication and benefit distribution are managed in wind park projects and how public and private actors interact within the broader institutional context. The analysis integrates high-level national policies with the detailed processes occurring at the case level, offering a comprehensive view of how rules, norms, and strategies shape the interactions and agreements between multiple actors involved in wind park development.

The findings of the thesis highlight several key insights. Firstly, the size and authority of the jurisdiction overseeing a wind park project significantly impact its development. Larger authorities tend to have more resources, which enables them to manage the project more effectively and engage with local residents. Additionally, location-specific planning and permitting procedures, particularly where pre-selected sites have been identified in land use plans, facilitate the development process by making information more accessible and improving the dynamics between local residents and project initiators. Early stakeholder engagement and thorough upfront planning are also critical to the success of wind park projects. Transparent communication with local residents from the outset is essential to reducing opposition and building trust, while a lack of engagement tends to foster resistance and delay development. Another important finding is that the current policies governing participation and benefit distribution are often inadequately implemented. More flexible policies that allow for interpretation by local authorities and are guided by strong principles rather than strict regulations tend to yield better results. Early engagement with stakeholders, combined with tailored compensatory measures that reflect the specific needs of different resident groups, leads to more efficient project outcomes. Understanding the differences between local resident groups, such as those living close to the project versus those living in nearby cities, helps to ensure that compensatory measures are appropriate and that development processes are shortened.

The research carries important implications for advising policymakers and project initiators. It emphasises the need for early and continuous engagement with local residents to build trust and reduce opposition. Moreover, flexible policies that allow for interpretation and are supported by clear principles are more effective than rigid regulations. Educating municipalities and expanding successful strategies, such as the Regional Energy Strategies, can further enhance the effectiveness of wind park projects. These steps can help authorities select competent project initiators and improve the implementation of participation processes. The scientific contribution of this thesis lies in its novel approach of linking high-level national institutions with case-specific participatory processes. By combining these perspectives, the research provides a deeper understanding of how formal policies and spatial planning laws are translated into practical actions at the local level. This dual-level analysis sheds light on the interactions between different actors and institutions, offering insights that had not been studied in such detail before. In doing so, the thesis contributes valuable knowledge to the field of institutional analysis and renewable energy development.

Executive Summary
Current Status The aviation industry is transitioning from traditional maintenance practices, typically scheduled after a specified number of flight hours, to more advanced, data-driven approaches. These predictive maintenance techniques leverage machine learning models to enhance the accuracy of assessments. These models can reduce the frequency of unscheduled maintenance and optimize inventory management. However, such models rely heavily on large datasets, which are challenging to compile in the aviation sector due to the rarity of specific operational incidents and the diverse types of data collected by different companies. When collaborating, a tradeoff arises between the level of security measures, trust in partners, and ensuring the system still functions. Federated Learning emerges as a promising solution to these challenges. Federated Learning is a novel form of machine learning that allows multiple entities to collaboratively develop a shared model while keeping their data localized, thus maintaining privacy and data sovereignty. Question The primary objective of this research is to identify and validate the critical architectural features necessary for the acceptance of Federated Learning in the aviation maintenance industry. Architectural features refer to design choices such as security protocols and governance frameworks. By focusing on these aspects, this study addresses the technical and collaborative challenges that must be overcome to develop a Federated Learning system for predictive maintenance in aviation. Resulting in the following research question:
‘What architectural features should be included in the design of the ‘Federated Learning for aircrafts’ predictive maintenance system’ to be accepted by the stakeholders in the aviation industry?’ Approach This study employs a Design Science Research methodology. The sub-questions for this research follow the steps in the Design Science Research methodology. This is not the case for the demonstration phase which was deemed not possible due to the conceptual nature of the design.
1.
What are the challenges in sharing maintenance data, and how do they impact data safety and collaboration? Through a literature review and interviews with stakeholders, the study identified concerns about data privacy, security, and competition. These challenges significantly limit data-sharing initiatives. (Problem Identification and Motivation)
2.
What specific technical requirements should the Federated Learning system have to address these challenges? Based on the interviews and thematic analysis, a list of requirements was developed. These include robust privacy mechanisms, transparent governance, ensuring model explainability, and clear accountability mechanisms. (Defining the Objectives for a Solution)
3.
What architectural features should be included in the Federated Learning design to meet these requirements? ArchiMate modeling was used to design a system incorporating these requirements. Federated Learning, combined with encryption techniques and a consortium-managed system, was the result. (Design and Development)
4.
How do stakeholders perceive the acceptability of the designed system? After the validation interviews and the expert session, three changes were implemented: Switching from
5
differentiating on the model version to differentiating on the usages. Improving explainability
by switching to Trusted Executive Environments and removing differential privacy. Traditional contracts to increase trust towards each other were also added. (Evaluation)
5.
What lessons can be learned from the development and evaluation of the Federated Learning system for future improvements? The research highlights the importance of continuously building trust among stakeholders. Furthermore, the token-based reward system based on contribution is a good incentive to be adaptable and develop long-term collaboration. (Communication) Results
To build trust, this study employs traditional methods like legal contracts and appoints a consortium as a neutral party. Transparent governance and involving a neutral, trusted entity is necessary to gain stakeholder acceptance and ensure long-term collaboration. Blockchain technology enhances the transparency of the consortium's operations, ensuring all transactions and data exchanges are recorded on an immutable database...

Wind energy is deemed important in the Netherlands in sustainable energy transitions. While Commercial Wind Energy Project Developers (CWEPDs) bring professionalism, Local Energy Cooperatives (LECs) promote local renewable initiatives. The Dutch Klimaatakkoord aims for 50% community-owned renewable electricity, but real-world implementation of community involvement is unclear. The Institutional Analysis and Development (IAD) framework helps understand 'rules-in-use' for decision-making. This study explores how LECs and CWEPDs interact with these rules, focusing on Dutch onshore wind farm projects. Using 14 case studies, the research compares organisational forms in project outcomes, employing interviews and written sources for data collection and statistical testing and QCA for analysis. Key findings include LECs completing projects faster and with fewer objections than CWEPDs and the importance of transparent information sharing. The study suggests policy and development implications, highlighting the need for more inclusive and transparent decision-making in wind energy projects.

The banking sector is currently dealing with Anti-Money Laundering (AML) issues, with stricter legislation in Know Your Customer (AML) being the instigator. De Nederlandsche Bank is pursuing fully explainable and transparent models with a risk-based approach. Councyl provides such a model: Behavioural Artificial Intelligence Technology (BAIT). This study aims to analyse the current environment BAIT would be implemented, to see if the tool fits the requirements of the banking sector. The inherent features of BAIT, adding to the four functions make it an interesting tool for banks to consider.

The modern power grid is becoming more susceptible to cyber-attacks due to an increase in digitalization, leading to a larger attack surface for malicious actors to attack. Such attacks on critical infrastructure could lead to partial power outages, minor societal disruption, or in the worst-case scenario, a rolling black-out in which the entire country has no access to electricity. Electrical utility companies can decrease the likelihood of a successful cyber-attack on the Cyber Physical Power System (CPPS) – consisting of the physical power grid, and vulnerable Information Technology (IT) and Operational Technology (OT)- by implementing cyber security interventions. Investing in these cyber security mechanisms is not cheap, which is why it is expected to have a certain return on investment. However, it is hard to quantify the effects of prospective cyber security investments. The main research question of this study is: “To what extent can cyber security measures decrease the risk of cyber attacks on CPPS substations?” This research question is answered by means of an implicitly mixed research approach that uses computer-assisted attack tree modelling and Monte Carlo simulation. The model is based on the publicly available technical system information of known suppliers of relevant substation components and other documentation acquired by means of multiple literature studies and document analyses. The change in likelihood and subsequent risk has been studied by extensively modelling the possible attack paths of a digital substation. This has been combined with financial analysis in the form of a societal cost-benefit analysis. As a result, potential cyber security investments can be evaluated on their merits in the form of risk reduction and their required costs as expressed in dollars. The contribution of the performed research to science is the elaboration of existing models to more accurately represent reality, and simultaneously provide the cyber security decision-making process with a tool that provides guiding Key Performance Indicators (KPIs). This study has shown that suggested measures from the quantified model are able to increase the TTCavg needed by malicious actors to reach their intended target, and therefore cause a decrease in likelihood and subsequent risk of the studied scenarios. An important finding of this study emphasizes the need for extensive attack path modelling. This finding was the fact that the application of some well-intended countermeasure (such as remote-attestation), might have no significant effect on the likelihood and risk of a certain scenario at all, but only changes the dominant attack path. While the constructed quantified model, as proposed in this study, is able to provide quantified insights into the effects of proposed cyber security investments, it is merely a simplified tool that should be expanded upon to generate more accurate insights. Besides the aforementioned there have been additional findings from this study. Such as a list of weaknesses in the current state of digital substation cyber security. This list has been created by an extensive document analysis of over 40 sources. Also, an overview of 23 different possible cyber security interventions has been compiled by a systemic literature review of over 16 sources. According to the quantified model, a reduction (between 21.8% and 93%) in the total risk of certain attack scenarios against digital substation by malicious actors can be achieved. The costs for these possible risk reductions range between $28 thousand for a honeypot deception system and $413 thousand for a combination of all the simulated countermeasures. These countermeasures could, in comparison to a base case with no protection, potentially reduce the total risk by an amount between $3.7 billion and $15.9 billion. According to the general societal cost-benefit analyses, the best Retun-on- Investment (ROI)/cost-effectiveness of investment is the investment in a honeypot (scenario 5) which has an ROI of 247,390, and the least cost-effective is the investment in remote attestation (scenario 4), which has an ROI of -2,066. Altogether, this study has shown that there is added value in using a simplified quantified model to aid in decision-making for digital substation cyber security investments aimed at risk reduction.

This research investigates the potential impact of blockchain based start-up crowdfunding. The emergence of blockchain and cryptocurrencies paved the way for a more decentralized finance system which offers alternative funding opportunities for start-ups. The scope of the research is to analyze the benefits and drawbacks of blockchain based crowdfunding for start-ups, and how it could impact the traditional financing models. The study adopts a qualitative research approach in which the Institutional Analyses and Development framework was consulted to find the key research concepts for the case studies. The research was conducted by analyzing data from blockchain protocols that consult their communities for important the decision making within its ecosystem. The findings suggest that blockchain based crowdfunding allows to raise funds for start-ups from a broader pool of investors, without the necessity to tap into the ecosystem that Silicon Valley offers, and a potential faster funding method than traditional methods. However, there are some concerns regarding trust and transparency with crowdfunding. The research found that adopting dequity, which is a new financial asset that combines both properties of debt and equity, can increase transparency and trust for the investors, while granting the crowd investors more control. The proposed solution is a dequity token with a governance that has quadratic voting, a voting multiplier and locking a large portion of the raised funds during the crowdfund. Overall, the research suggests that blockchain based crowdfunding can offer new opportunities for entrepreneurs to raise capital, in an inclusive manner, without the need of venture capitals or banks.

The Paris Agreement seeks to limit global temperature rise and achieve climate neutrality by 2050. The UN's Sustainable Development Goal 7 emphasizes sustainable energy access, with smart grids playing a crucial role. These grids adapt to user behavior and interconnect various components. While smart grids address challenges in integrating renewable energy, their complex socio-technical nature demands a multidisciplinary approach. Institutions are vital in smart grid development, but their importance often goes unnoticed. The interplay of stakeholders, technology, and institutions complicates smart grid implementation. Although existing research largely focuses on technical aspects, a holistic approach considering social, economic, environmental, and institutional dimensions is essential. This study aims to bridge this gap by employing the Institutional Analysis & Development (IAD) framework to enhance the Netherlands' electricity/energy system design, particularly at the community level, concerning the impacts of institutional rules on smart grid development and implementation.

The chosen research approach is a comparative case study, focusing on smart grid integration in the Netherlands. The Institutional Analysis and Development framework aids in analyzing complex socio-technical systems, particularly energy transitions and smart grids. Three cases were examined: Schoonschip Amsterdam, GridFlex Heeten, and Groene Mient The Hague. Data collection involved literature review and 14 semi-structured interviews with experts and stakeholders. Interview data was deductively coded using the IAD framework, facilitating comparative case analysis and cross-case analysis using the ASI diagram (Actors, System & Institutions).

The transition of the Dutch electricity system into a competitive liberalized market has increased the importance of smart grids for sustainability. Decentralized energy production, facilitated by smart grids, requires technological advancements and institutional changes at various levels. Successful smart grid implementation in the Netherlands involves obtaining exemptions, feasibility studies, and engaging legal entities like homeowners' associations and energy cooperatives. Cases emphasized the significance of pre-exemption feasibility studies, technological requirements, stakeholders like Distribution System Operators, and energy management systems.

The findings highlight the uniqueness of each case and the role of institutional rules. In Schoonschip, boundary, position, choice, and payoff rules were pivotal. GridFlex highlighted position, choice, information, and scope rules. Groene Mient showcased boundary, position, choice, payoff, and scope rules. These case studies underscore complexities in decision-making and governance within smart grid initiatives. Successful implementation requires considering technology, institutions, and stakeholders as indicated by the ASI diagram. The IAD framework analysis reveals the impact of biophysical conditions, community attributes, and rules-in-use on smart grid outcomes.

Recommendations include further research on evolving rules, understanding actor positions, interdisciplinary research, integrated decision-making frameworks, empirical case studies, and policy recommendations such as financial support, clear policy roles, local smart energy systems, positive framing, standardized data exchange, and umbrella organizations for cohesive local smart grid projects.

In conclusion, the study underscores the intricate nature of smart grid implementation, emphasizing the need for comprehensive consideration of technology, institutions, and stakeholders. The IAD framework sheds light on the complex dynamics, guiding effective decision-making and policy formulation for successful local smart grid development.

This paper explores the potential of smart contracts in the reinsurance industry to address escalating non-productive costs driven by market dynamics. Reinsurers, aiming to enhance stability amid increasing inflation, claim severity, and frequency, have adopted stricter underwriting criteria and raised premiums. This has led to a more detailed drafting of reinsurance contracts, reminiscent of the formalization trend that began in the 1970s. However, this formalization has inadvertently inflated administrative and dispute resolution costs, counter to the industry's core objective of efficient risk and capital allocation.

The study delves into how smart contracts can offer a solution. Smart contracts are computerized protocols that can automate contract clauses, potentially aligning better with industry goals. The examination focuses on their impact on transaction costs, involving expenses incurred by insurers and reinsurers to execute transactions.

Findings reveal that smart contracts can effectively reduce administrative costs by automating tasks, particularly in high-volume and standardized scenarios. However, their effect on dispute resolution costs is more nuanced, as the reinsurance sector still benefits from human interpretation.

In conclusion, while smart contracts hold promise for reducing transaction costs in reinsurance, the industry's unique complexities and high financial stakes may pose challenges and necessitate post-implementation adjustments. The paper recommends exploring smart contract applications in industries with smaller disputed amounts and lower trust levels than reinsurance.

Blockchaintechnologyhasintroducedthepossibilitytoexchangevalueandinformationthroughtheinternet without the supervision or permission from any third party intermediary. While many private and public entities have seen this as a chance to increase their operational efficiency, other ventures have been utilizing this sameedgeasameantodevelopnewinstitutionalregimesgiventheimpactontransactioncostreduction, The theories central to institutional economics claim that transaction costs are the ultimate driver for shifts in institutional settings. The purpose of the following research was to investigate whether the reduction in transaction costs allowed by blockchain technology is effectively causing new forms of institutions to arise. Given that blockchain is still in its infancy, there is a current lack of academic literature addressing its governance edge. Furthermore, the few studies available adopt a comparative approach, hence failing to explore the entirety of new institutional possibilities. For this reason, the following research utilized the Institutional Analysis and development framework developed by Elinor Ostrom as a holistic and institutional agnostic methodofanalysis. The particular application analyzed was the one of financial product exchanges. Today, the exchange of financial products as securities, is coordinated by hierarchical organizations, namely firms. As a matter of fact, multiple different firms are involvedintheexchangeoftheseproducts. Exchangesareresponsibleformatching buyers and sellers, clearing houses assume counter party risks and make sure the trading parties can meet their obligations, and custodians and central security depositories are responsible for the final settlement of transactions. Furthermore, reconciliation costs are incurred by all participants when updating their respective databases. Given that blockchains are fundamentally public distributed records of transaction, they effectively undermine the economic efficiency and logic of the current systems of clearing, settlement andreconciliation. Decentralized exchanges are tackling these issues today. For the purpose of the analysis a total of three decentralized exchanges were shortlisted from the over 200 existing. The first filter of choice was to choose between exchanges native to the Ethereum blockchain. This because, being the oldest in the space, they present the most well developed and resilient communities apt for the analysis. Next, exchanges were further filtered by total liquidity in the protocol, or total value locked, and trading volume. The three exchanges selected for the analysis in the end were Uniswap, Sushiswap and Curve. Given that these protocols run on a public and permissionless blockchain, it was possible to retrieve large amounts of data on their relative performance. A number of data acquisition and analytics platforms were utilized for data analysis and visualization namely, Dune, Tally and Sybil

To provoke the needed energy transition, European Union (EU) countries initiated significant offshore wind energy investments in the North Sea. However, there also exist adverse aspects to the use of higher renewable electricity levels:

• renewable energy sources, such as offshore wind energy, may threaten the security of our energy system, since they are characterised by a high variability, and limited predictability and controllability;

• the effectiveness with respect to decreasing greenhouse gas emissions is limited, because electrification can be not applied within all sectors, such as heavy industry and heavy duty transport;

• a large increase in offshore wind capacity requires moving further off shore. This results in relatively high costs because of the energy losses within the (longer) electricity cables.


One way of coping with these challenges, is by producing hydrogen offshore, by means of wind energy, and transporting it to shore by for example repurposing the existing offshore natural gas infrastructure. Such an offshore hydrogen system located in the North Sea might sound favorable; however, the feasibility of such a system on this scale is yet to be determined.

In this thesis the possibility is investigated to design a future proof offshore hydrogen system. Such a system would consist of (current as well as new) wind farms, electrolysers to produce hydrogen by using (a part of) the electricity generated by the wind farms, and an infrastructure to bring the hydrogen to shore. Given the EU investment plans in offshore wind energy, a phasing period is used from 2030, 2040, to 2050. This research is done by:

• deriving multiple hydrogen system designs by for example optimising the transmission infrastructure;

• analysing the supply potential of these system designs.


The results show that a cost-competitive hydrogen system in the North Sea can be realised. The proposed system design has a Levelised Cost Of Hydrogen (LCOH) of 2,08 EUR/kg and a positive Net Present Value (NPV) for the most relevant pricing scenarios. This LCOH is relatively low compared to other researches, which are mostly between 2 and 3,5 EUR/kg.

An interesting result concerns including refurbished pipelines of the existing offshore gas infrastructure. When using only new pipelines, the transmission infrastructure costs increase with 36%. Furthermore, the results show that it is more cost-efficient to downscale electrolyser capacities than to use the peak of the available electricity to determine the capacity of the electrolysers. Additionally, the productivity of the wind farms can increase up to even 220% by using the different electricity surplus for hydrogen production.

Based on this research, recommendations can be given:

• National governments should formulate policy on whether or when gas extraction in the North Sea should stop. Thereupon, the (energy) transmission system operators should scope their plans towards transporting offshore hydrogen to onshore, as well as start planning the onshore hydrogen backbone.

• The EU should decide whether to build one interconnected system in the North Sea, or multiple isolated (per country) hydrogen systems. Based on this decision, it is important to start shaping rules and standards for hydrogen trade, as well as determining regulatory regimes to support offshore hydrogen production.

• Further research should be done on the electrolyser costs and efficiencies, as well as the different types of electrolyser locations; on the possibilities of hydrogen storage; and, to include (regional) hydrogen demand values.

The Architecture, Engineering, and Construction (AEC) industry is undergoing digital transformation to improve project performance and Building Information Modelling (BIM) is helping to achieve this objective. BIM is a comprehensive digital representation of a built facility with great information depth. BIM fosters collaboration across the construction supply chain however the benefits of collaboration are realised only in a relatively small number of BIM-enabled projects. Poor collaboration continues to be one of the major risks affecting BIM-enabled projects with misunderstandings, misinterpretation of data and increased rework. This is especially high in the design phase of construction projects because of the high BIM usage. This research aims to explore the most critical barrier to collaboration in BIM teams in the design phase of infrastructure projects and provide strategies to avoid the barrier in future projects. The most critical barrier in this research is defined as “the main issue that causes the occurrence of problems resulting in undesired outcomes”. For this objective, the case-study method is preferred. Three large infrastructure projects are studied and interviews were conducted with BIM teams to identify the barriers to collaboration in BIM teams. The data collected are analysed by comparing among cases and with literature. Why questions were asked until the main issue that led to the occurrence of problems is found. From the analysis, it is found that lack of guidelines and standards is the most critical barrier to collaboration in BIM teams in the design phase of infrastructure projects. To avoid this problem in future projects, the existing BIM-based design process is improved to work with ISO19650 standards along with strategies to achieve better results. To enable better collaboration in BIM teams, the process is automated using Business Process Management (BPM) software. The proposed strategies are validated with BIM teams for applicability in practice. To conclude, the proposed strategies help in solving the lack of guidelines and standards. It improves the BIM-based design process in infrastructure projects and leads to achieving better collaboration in BIM teams. This research benefits researchers as well as BIM teams. Researchers can validate the proposed strategies in real-life projects and BIM teams can improve their work practices by implementing the proposed strategies.

In recent years, false information on social media platforms has become a centre of attention due to its consequences on elections and public health. COVID-19 has shown how an infodemic fuelled by false information can be detrimental to public health. Existing measures are ineffective. Thus, the thesis takes an exploratory approach from the lens of new institutional economics. A comparative institutional analysis between public health institutions and social media platforms concerning their information discovery process and how differences between them can lead to false information on social media platforms is performed. The findings question whether false information is as widespread on social media platforms as projected, identifies the role of public health institutions and politicians in the spread of false information, how information is regulated on social media platforms and what drives the information discovery process on social media platforms. Based on the comparative institutional analysis, it is recommended to implement prediction markets to address false information on social media platforms.