By the year 2050, the Dutch government aims to have transitioned away from natural gas and provide heating to all residential dwellings using more sustainable technologies. Heat pumps (both all-electric and hybrid models) are expected to play a large role in fulfilling this heating demand. However, because heat pumps require electricity in order to provide heating, a rise in the number of installed heat pumps is expected to have a significant effect on the residential electricity demand. This poses an issue during times of peak demand and low renewable power availability, leaving no choice but to increase reserves and ramping needs from fossil-fueled conventional power assets. Luckily, when provided with the right control strategy, heat pumps are able to flexibly draw electricity from the power grid, lowering the electricity required during peak hours. As a result, it is both possible that an increase of installed heat pumps in the Netherlands can either aggravate the flexibility problem or provide a key role in solving it. The extent to which heat pumps could contribute to providing this demand response is not only dependent on the technical demand-response characteristics of the heat pumps. Rather, it is determined by both the willingness of households to adopt the heat pump and the way users react to the proposed control strategy. Using econometric techniques, the master thesis research project quantifies these behavioral dimensions into a model estimating the electrical flexibility heat pumps will be able to either require or provide in the Netherlands in 2050. In the research, several consumer typologies are defined distinguishing between consumer user groups and consumer adoption groups. The research finds that an additional 1.6 GW of flexibility can be provided by heat pumps in 2050 should policy be developed that targets consumer adoption behavior. Consumer user behavior has less of an influence on the average demand response potential of heat pumps, however could be an area of focus should a frequent demand response be desired.

Hydrogen technology offers a promising opportunity to mitigate problems that arise due to the energy transition. It can enable storage and balancing for surplus in electricity production from intermittent resources as solar energy and wind power. Electrolysis converts green electricity into hydrogen. Hydrogen is suggested to be introduced into the current natural gas grid as sustainable replacement of natural gas in households. This allows for reduction of CO2 emissions and heating buildings that are not easily insulated. The number of poorly insulated homes is difficult using electrical low temperature heat pumps. Even though hydrogen offers many functional advantages, there are potential barriers that may restrain the introduction of hydrogen into the energy system. Often siting of large-scale renewable energy technologies such as wind turbines and solar panels, causes anxiety and societal opposition. The reason for opposition is often distrust in institutions and technology that fails to equally distribute costs and benefits amongst stakeholders, authorities and residents. To prevent this for hydrogen infrastructure, this research studies how hydrogen technology can incorporate local values. This allows for adapting technology to local needs and involving households in decision making. Acceptable technology and a responsible process increase trust and acceptance when the infrastructure is actually adjusted. This research answers the question which technical design choices provide for an acceptable and responsible design of hydrogen distribution and domestic use to households in Stad aan ´t Haringvliet. Stad aan ‘t Haringvliet is used as case to research how to identify values and capabilities that play a role when designing hydrogen infrastructure for a specific local context. A participatory modelling approach is developed and executed. The approach involves stakeholders and households in the process of developing an Agent-Based Model. The model simulates the acceptability of hydrogen technology for households and identifies issues to be addressed in the next project phases. The Capability Approach and Value-Sensitive Design form the conceptual basis of this research. The effects of technology on households is assessed in terms of valuable capabilities and individual well-being. Capabilities create well-being as they allow users to choose e.g. how to heat and what technology to buy. The outcomes of this research are specific local requirements and general knowledge on how to develop acceptable technology. Hydrogen infrastructure is acceptable when it has only a small impact on households and offers comfort and security against the same costs as the current system. Higher costs are possibly acceptable when the new system reduces CO2 emissions or offers higher comfort. However, technology alone is not able to incorporate the whole set of important values. Additional institutional measures as for example information, participation and support are required to gain long term acceptance.

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 increased usage of Electric Vehicles and Renewable Energy Sources causes issues regarding the balancing of the electricity grid. To avoid investment costs, Distribution System Operators desire flexibility solutions. One of these flexibility solutions is the usage of the battery of the Electric Vehicle as an electricity source. This concept is known as Vehicle to Grid (V2G). However, providing V2G services might cause discomfort for the Electric Vehicle user. A contract can be used to compensate the user for the experienced discomfort. Literature on these contracts is lacking and the behaviour of EV user to these contracts is unknown. This study aims to close this gap. Data is collected by means of a web survey and evaluated with a multinomial logit model. It is shown that the difference in expected demand for price- and volume-based contracts is minimal. In addition, three contract elements can solely increase demand for V2G but require high levels. More value is created when a combination of these three contract elements is used. It would be valuable to understand how and where V2G can provide value. To do so, the results of this study can be used as input for a dynamic model that evaluates day-to-day electricity supply and demand.

Global Earth Systems Integrated Assessment Models are used by policymakers to understand the complex interactions between anthropogenic activity, including energy use, and its environmental impacts. However, they are computationally expensive, so spatiotemporal resolution is kept low as a trade-off. Generally, the smallest geopolitical component is a large nation. Yet, much of energy transition planning occurs at subnational levels. Downscaling is the process of turning low resolution model outputs into higher resolution ones and has been used extensively in hydrological and weather modelling. Since downscaling in the energy sector is nascent, this presentation reviews the trade-offs between statistical downscaling methods within the energy domain using ten newly developed criteria. Highlighting such trade-offs can better equip policymakers as they craft energy transition policies. These criteria fit within three main categories: replicability, coherence to the parent model (the IAM), and handling of energy-specific insights. The linear downscaling method was highly replicable and, though coupled to the parent model, assumes that all its constituent regions are homogeneous and distributes outputs blindly. It performs well to introduce technologies that do not exist yet, but does not consider geographic to resource use here. The convergence method similarly struggles with geospatial limitations, but obfuscates energy sector nuances described by the original model. It is less replicable than the linear method. Modifying these approaches could resolve some of their issues, but they will likely never be useful for serious policymaking. Two other methods described in the literature are also discussed that could overcome the limitations of these two approaches, though they are not implemented here. Downscaling energy systems still holds some promise, though significant research is required to integrate technological innovation and diffusion considerations to the methods.

Increased Feed-in of renewable energy sources and an increase of sales of electric vehicles (EVs) complicate balancing of demand and supply. EVs are considered core enablers for dealing with intermittency due to their storage potential. This does however require that these EVs are charged according to smart charging protocols, which is a vehicle-to-grid (V2G) technology. Options exist for designing such a system. However, due to the gathering of personal (transactional) data, and the involvement of monetary assets, ethical concerns such as privacy and trust issues are assumed to arise at or after the introduction of such a technology. This has been the case with for instance smart meters. The effects of such ethical concerns on the usage of smart charging systems are unknown. The main research question for this thesis is: “How can a smart charging system be designed which is both used on the short- and long-term and fulfils ethical values of EV owners?” The purpose of this thesis was to explore and assess different smart EV charging system designs concerning factors contributing to system performance and possible ethical concerns. The research was conducted by creating an integrated framework of both the capability approach and complex adaptive systems. Agent-based modeling was used as the main research method in order to model the behaviour of EV owners within an smart charging environment. The model aims at providing valuable insights concerning which system design performs best with respect to system performance and ethical value fulfilment. Several architectural design decisions are elaborated on with respect to a decentralised or a centralised system. The research outcomes indicate possible short- and long-term ethical concerns of users with respect to the designed system. The effects of these concerns are at this point unknown, but are considered to have an ongoing effect on the performance of the system. The research objectives involve the identification of four key architectural design decisions which consist of both decentralised and centralised alternatives. The conceptual framework built upon the capability approach, the unified theory of acceptance and use of technology (UTAUT), and complex adaptive systems is used as conceptual basis for the agent-based model. The experimental design revolves around comparing three experimental design alternatives: a (1) public centralised system, (2) public decentralised system, and (3) a private decentralised system. The public centralised system describes a system which is controlled by a single authority, and in which data is stored within an external database. Furthermore, participants are free to participate. The public decentralised system describes a system which is not controlled by a single authority, and in which transactions are validated through shared consensus. Within the private decentralised system, power is exerted towards a single facilitator, which is authorised to whitelist participants. Aside from whitelisting privileges, the platform is not controlled by a single authority. The combined theoretical framework induces a wide variety of uncertain parameters. Therefore, the experimental designs are explored through an EMA study. The main purpose of this EMA study was to assess the three designs with respect to system usage, system performance, and ethical value fulfilment across a wide range of possible scenarios. The model outcomes provide knowledge regarding promising design directions as well as directions for further research. The developed model describes the interactions between EV owners and the smart charging platform. The performance of the system is based on three constructs originating from UTAUT. These constructs are: performance-expectancy, effort-expectancy, and social influence. The total performance of the system combined with the personality traits of the EV owners determine whether an EV owner uses the system. Effort-expectancy describes the degree of effort needed to participate on the platform. Social influence is rooted within the capability approach and incorporates a combination of effects resulting from direct and indirect interaction between EV owners. Electricity is traded through the use of the platform which creates demand and supply. For model experimentation the KPIs for assessment are: the number of system users, the number of transactions, the number of traded kilometers, and the selected ethical values. Regarding ethical values, the experimental outcomes provide strong indications that a decentralised system scores best on privacy, security, anonymity, and confidentiality. The scores indicate that problems regarding these ethical values are less expected on the short- and long-term as compared to a centralised system. When aiming to design a decentralised EV charging platform, special focus should be placed on achieving trust, as indications are present that trust issues could arise for decentralised systems. Concerning the KPIs related to system usage and performance, the results indicate that a centralised system is highly preferred. For each of the KPIs (number of users, enabled users, number of transactions, and number of traded kilometers), a centralised system scores higher. However, due to the large number of users, centralised systems have high oscillations in demand and supply. Concerning the main research question, the research outcomes indicate that when designing a smart EV charging system, one should consider both decentralised and centralised design elements. Both elements are needed as solely focusing on either centralised or decentralised systems has implications for value fulfilment as well as system performance. The optimal combination of design elements is at this point uncertain. The research outcomes clearly indicate that extended research in this field is justified. It is important to further identify which system components in both centralised and decentralised systems positively contribute to the chosen KPIs. These components can then be combined in order to work towards an optimal system design. The operationalisation of the capability approach opens new possibilities to assess these combined design alternatives with respect to system usage and ethical values. In that sense, this thesis provides a tool for assessing the designs of smart charging platforms by providing an integrated framework of the capability approach and agent-based modeling. The research described in this thesis has several implications for designing smart EV charging systems. The capability approach was found to be a proper method for assessing ethical values concerned with the usage of technology. Furthermore, it was found useful to extent the capability approach with other theories and methods such as the unified theory of acceptance and use of technology, complex adaptive systems, and agent-based modeling. Using this approach for assessing smart EV charging systems helps to keep focus on the actual well-being of system users, rather than solely focusing on technological performance. Furthermore, the approach is long-term oriented. Directions for further research are presented. Essentially, these directions stimulate the further exploration of feasible smart charging system designs by extending the scope of design alternatives, further explore the integrated approach of CA and CAS, and further quantify the agent-based model.

This research has been performed to help the Dutch government with the decisions and choices it has to make in order to meet the requirements of the Paris Agreement and the Climate Agreement. Decentralized energy systems can be a solution for meeting these requirements, however trade-offs have to be made between different system criteria, which are evaluated as values in this research. The aim of this research is to find out what strategies can reduce and deal with value conflicts that can be identified when designing a decentralized energy system considering the properties of a city district. A specific city district is analyzed, i.e. 'De Vruchtenbuurt' in The Hague, the Netherlands. The research starts with an identification of values, conversion factors, governance models and technical designs which are important when changing to a decentralized energy system in 'De Vruchtenbuurt'. Following the Capability Approach is used to structure the different components and the relations between them. Subsequently, a translation of the components into a simulation model is made, using the values to evaluate the acceptability of the capabilities. By analyzing the values that are conflicting (for different conversion groups) for different input parameter settings, insights are gained about possible value conflicts that can emerge in 'De Vruchtenbuurt' and possibly also in other city districts. Finally, an analysis on strategies is developed that can be effective to limit the emergence of the identified value conflicts and therefore possibly support the change to a decentralized energy system in 'De Vruchtenbuurt' and in other city districts. Policies (financial instruments and supporting innovations) that create more feasible solutions for the heterogeneous households are effective to limit the context dependent value conflicts. It is not realistic to eliminate all value conflicts, since some of the values are inherently in conflict with each other. For these conflicts thresholds can be formulated that should be met for every value. Given that value conflicts exist, focusing on the process of the change to a decentralized energy system is seen as important factor for support and participation.

Predictive maintenance (PdM) is one of the promising technologies coming along with the fourth industrial revolution being pushed by disruptive technologies like Internet of Things (IoT), Artificial Intelligence (AI), robotics and Augmented and Virtual Reality (AR/VR). Adopting PdM potentially allows companies to reduce equipment downtime, increase the safety of their processes, increase revenue and develop additional business models. Although the promises of the technology are extensive, the successful adoption rate of this technology is still relatively slow. This is stemming from PdM’s multi-disciplinary nature and “hype” that over-promised its ease of implementation. Organizations are now starting to understand what is needed for efficient implementation, and this helps to manage the expectations about this technology. The fundamental problems highlighted in this research are the complexity, unclear vision, lack of knowledge and know-how in adopting AI predictive maintenance technologies inside an organization. According to Bain & Company’s survey companies in the industrial sector indicated that implementing IoT inside their organization proved to be more complicated than anticipated (Schallehn, Schorling, Bowen, & Straehle, 2019). There is a knowledge gap in the scientific literature, where a lack of best practice methods in terms of predictive maintenance implementation can be identified. Based on the problem highlighted and knowledge gap, the main research question was formulated: “How to facilitate the adoption of Artificial Intelligence-based predictive maintenance technology in the manufacturing industry?“. This study follows a phase-wise approach to obtain the research results. In the first phase, a literature study is conducted to identify the current situation about PdM, what information is available about the factors affecting this technology’s adoption and where is the knowledge gap to be filled. Selected factors to focus on with this research are discussed and agreed upon with the researcher and supervisors. In the second phase, the development of the best practices checklist is commenced. The centrepiece of this phase and the research project overall is the set of semi-structured interviews with 11 industry experts with extensive domain knowledge about predictive maintenance to collect best practices in PdM implementation. The insights gathered from the interviews are analysed in-detail in multiple iterations and then that filtered, aggregated information is used to develop the predictive maintenance project reference checklist. In the third phase, expert panel evaluates the practical applicability, generalizability and the validity of the constructed PdM checklist. Efficient implementation of PdM inside the organization could face numerous barriers and difficulties. Most of these barriers related to technologies using big data could be divided into three categories: technical, organizational and people related (S. Li, Peng, & Xing, 2019). Addressing all of these barriers in those 3 major categories would be unwise since that would not provide sufficient depth of analysis for each one of them. Selection of barriers is based on 3 criteria: the barriers must be relevant and applicable to the adoption of the PdM technologies; there should be a noticeable knowledge gap about how to overcome the barriers; the barriers must be complex enough (affecting multiple layers and stakeholders of the organizations) to fit with the Management of Technology multidisciplinary problem-solving perspective. Based on the information from scientific literature and consultancy reports on PdM, 3 relevant barriers to be focused on are chosen: business case building for PdM; trust in AI-based PdM (lack of trust in big data analytical results) and data management for PdM (the challenge of collecting the data, utilizing it and making sense of it). The interviews with the industry experts revealed valuable insights about predictive maintenance adoption, factors affecting the implementation and best practices that other companies have followed during the process of PdM realization. The most notable best practice that all the interviewees mentioned was involving all the relevant stakeholders early on. In addition, taking small steps, maintaining PdM platforms, celebrating small successes, showing a broad picture and providing a range for PdM business case were outlined. Furthermore, key factors that emerged from the conducted interviews influencing PdM adoption are delineated and summarized in this research project. These are useful for both practitioners and academic personnel who have an interest in this domain and want to gain further understanding of the dynamics surrounding predictive maintenance projects. This research project developed best practices reference checklist for predictive maintenance project implementation that supports organizations on high-level in adopting this novel technology by illustrating and bringing awareness to best practices that other organizations have been following during PdM implementation. This reference checklist is constructed to be a holistic, high-level PdM project support tool for the stakeholders proceeding with predictive maintenance implementation for the first time. This means that a detailed analysis of separate nuances is not sought after since that would misalign with the goal of being a wholesome, comprehendible overview of PdM project implementation checklist. Having a clear, structured and holistic perspective allows stakeholders to conveniently follow this checklist commencing and during predictive maintenance projects without being overwhelmed by excessively detailed information. This best practice checklist based on empirical study comprises a five-phase approach where the enablers and barriers in each phase are mentioned and suggestions on how to deal with them are outlined. These 5 phases are as follows: concept, feasibility, data, PdM algorithm development and operation phase. Furthermore, high-level, structured steps in each phase are laid out to support and offer recommendations to organizations with their PdM activities. In the end of each phase, an overview of best practices and barriers is delineated to recapitulate. In the concluding section of this best practices checklist, a compact, five-page adaptation of this reference checklist is devised for a quick overview of this constructed PdM project support medium and it is advisable to resort back to phases in the checklist itself if the more detailed explanation is needed. This compact version is meant for practitioners in the industry who have strict time limitations and wish to receive information quickly in a condensed format. To the best of our knowledge, such kind of high-level compact overview to assess PdM projects was not existing in the scientific literature. This research project directly investigates and provides a best practices checklist to fill this gap. In addition, this research provided design improvement ideas for different stakeholders to incorporate in their processes/products to facilitate better adoption of PdM. Trust factors affecting the implementation process of predictive maintenance are also outlined, helping companies to better communicate with their clients and internal organization about the benefits and usefulness of PdM. The developed research output has been preliminarily validated and evaluated by the expert panel that concluded that this best practice checklist indeed supports organizations in adopting predictive maintenance technologies. Furthermore, it was agreed that the output is clear and understandable with a well-structured approach. Coming from the high-level nature of this research, experts agreed that this research is generalizable to other industries. Main recommendations (for future research) include validating the best practice checklist in practice with multiple organizations inside the industry to correlate usage of this approach and success factor of implementing PdM. Furthermore, the development of additional support tools and frameworks to facilitate efficient implementation of predictive maintenance technologies would yield increased adoption rates of the technology. This research highlighted important factors contributing to the adoption of predictive maintenance technologies from organizational, people and technology perspectives. This helps to create more awareness about what is needed to consider for better adoption of this technology. Furthermore, a high-level structured overview of best practices checklist supporting PdM implementation is contributed to the scientific and practical domain, filling the previously outlined gap in the literature. In addition, coming from the analysed literature, this research complements the scientific literature on the topic of predictive maintenance by providing original content and additional awareness to the overall academic context regarding the dynamics of this technology’s adoption.

In order for the Netherlands to reach CO2 neutrality by 2050, large investments in zero emission technologies are needed. These investments would comprise out of renewable energy generation, higher energy efficiency alternatives and electrification of end uses. Although climate mitigation has become an ever growing societal concern since the ratification of "het klimaatakkoord", progress in the industrial sector has been seriously lagging in the Netherlands. In 2021 the reduction of CO2 emissions in the Netherlands stagnated and the emissions of the industrial sector actually slightly rose. So too in the Port of Rotterdam, where the chlorine cluster is not showing any significant CO2 reductions. There are several potential explanations why there is a gap between what should be invested and what actually is invested. They define the gap as, ’the apparent reality that some technologies that would pay off for adopters are, nevertheless, not adopted’. So, why do decision makers under invest in zero emission technologies? The explanations of fall into two broad categories: •Market barriers •Behavioural barriers Following the problem situation, this thesis focused on researching to what extent market & behavioural barriers contribute to the investment gap in the Port of Rotterdam Chlorine cluster, by incorporating both market and behavioural barriers in a quantitative investment model. The incorporation of these barriers in the model result in a range of investment types, some of them non-optimal and varying in perspectives on valuing the future. By simulating technology adoption under the assumption of this range of varying configurations of market and behavioural barriers, it is possible to determine the effect of those two categories of barriers on technology adoption at the PoR chlorine cluster. The obtained insights of this research feed into the larger study of quantitative decision models for the industrial sector. Where the following main research question is answered: What is the effect of market and behavioural barriers on zero emission technology adoption at the PoR chlorine cluster? To answer the main research question, the model represents the PoR chlorine cluster on a highly detailed level and bases it's technical system's configuration on a thorough plant-process-product & zero-emission technology inventarisation. These two inventarisations give the current and possible future configuration of the PoR chlorine cluster's technical system. Consequently, this makes it possible to explicitly model the technology stock at the chlorine cluster and determine technology adoption on an asset level. The market & behavioural barriers are represented by 8 evaluation types. These evaluation types represent 8 varying configurations of the market & behavioural barriers. Consequently, a scenario analysis was conducted with the model. The scenario analysis resulted in transition pathways, total CO2 emissions and total cash flows between 2022-2050. The model results show that the incorporation of market and behavioural barriers lead to postponed adoption of zero emission technology adoption. This is reflected by the lower number of years that these alternatives are installed between 2022 and 2050. The lower adoption lead to an increase of 288% total CO2 emissions between 2022-2050, compared to the optimal solution. Underneath, the key findings from the model results are presented…

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

Large-scale infrastructure projects such as ports can have a significant impact on the development of nearby cities. While economic benefits of the port spill over to other regions, often internationally operating firms, port-cities experience negative externalities like environmental and land use issues and traffic congestion. Traditional port impact studies are expensive, and offer a static picture that doesn’t take into account the complex adaptive nature of the port-city system. There is a need for a comprehensive dynamic model of port-city development, incorporating beneficial and negative externalities of port infrastructure development on the city and peri-urban green space. A system dynamics modelling study is performed using the case study of Tema and its port on the Gulf of Guinea in Ghana, to evaluate interventions for sustainable port-city development in an African context. A model is conceptualized and specified, based on Forrester’s Urban Dynamics model, fit to cope with relative scarcity of available data. It incorporates and explicitly models road transport congestion, informal settlement and green space encroachment, as well as port infrastructure. Model testing and use show that the model is fit for the purpose of exploring the long term impacts of port infrastructure investments on the port-city system. Interventions aiming at sustainable development are simulated and evaluated for their performance. Recommendations regarding interventions and the potential future use of the model with stakeholders are made.

Increased pace of developments strain the ability of policy makers to be timely and sufficiently informed. While there are already sufficient methods available for gauging what plays a role, topic modelling is a novel method that has the potential to be deployed at high speed with low effort. Values play an important role as it shapes policy making and in turn affects stakeholders. A semi-supervised topic modelling method called correlation explanation (CorEx) was used for this purpose as it allows steering the model to find aforementioned values. Green hydrogen is used as a case study where topic modelling is used to find values in scientific literature on this subject. Green hydrogen is envisioned to play a more important role in the context of decarbonisation. Such a transition has a major influence on society engaging business and households alike. Three different value sets are used reflecting different perspectives. These perspectives focus on corporate values, public values and the context in which hydrogen is discussed respectively. It was found that using topic modelling to identify values is highly constrained by its given inputs and processed outputs however. A methodological framework is therefore proposed. It suggests how topic modelling can be conducted for the purpose of identifying values playing a role in a certain domain. This framework consists of five components which are value definition, corpus selection, language processing, topic modelling and result interpretation. Utilising this framework helps with structuring the topic modelling procedure and identify bottlenecks in result quality.

Many societal, environmental and technological challenges can be characterized as wicked problems by virtue of being difficult to understand, define and solve. examples include sustainable management and consumption of resources, resilient technical infrastructure or curbing plastic pollution of the oceans. One method of tackling such wicked problems is the use of computer-aided modelling and simulation. Model-based decision support is a growing discipline involving the use of computer models of complex systems to explore, understand and manage them. A core concept in model-based decision support is scenario discovery. In scenario discovery, a model’s inputs and outputs are related to understand under which conditions policy-relevant outputs may occur. In a first step, a diverse set of inputs is used to generate a variety of outputs. In a second step, the subset of decision-relevant outputs is identified among the outputs through some external criterion, such as a threshold value. Finally, the inputs which generated those outputs of interest are identified, and a generative rule set is induced which usefully predicts under which conditions an input will generate an output meeting the external criterion. This rule set bounds an input subspace of interest, from which (most of) the outputs of interest originate. While scenario discovery performs adequately for quasi-linear and simple models, it is not well suited to behaviorally complex, nonlinear models. This is both because external criteria are hard to define for complex model behaviors, and also because there are often significant interactions and dependencies between model inputs, which current rule induction algorithms have trouble identifying.......

Carbon dioxide can be used during concrete production, which leads to stronger concrete as well as a sequestration method for CO2. While these technologies are developed quite far, they are currently not being used within society nor is there any systemic overview of the system and a reason why these technologies are not used. This thesis performs a Technological Innovation System analysis to investigate the Dutch concrete system and find barriers to the transition from conventional to carbon-cured concrete. The three located barriers are knowledge exchange, guidance of the search and the formation of markets. Intervention tools are presented to decrease the barriers and stimulate the technology within the system to enable further growth.

Today’s energy system has originally been developed based on central energy production and a passive consumer, whose interests have to be represented by the energy suppliers and distribution system operators. However, the industry is quickly changing. The share of renewable energy is growing, and consumers are increasingly involved in the production of energy. As a result, the share of intermittent energy sources increases and so does the need for flexibility in the energy industry. Therefore, it is questioned whether the current system in the energy industry still fits with today’s developments. To allow for flexible demand and supply of energy, digital technology for communication between computers or devices and electricity providers and consumers will become necessary. This will help to balance production and consumption at each time resolution, without high costs and unnecessary bothering of consumers. Blockchain is a technology that could potentially serve as a solution for a new energy industry system. Blockchain enables direct and reliable transactions of assets, between every party willing to do so, without the need for an intermediary or central party in control...

Barriers to CO2 abatement impede their materialisation. In this research the Y-factor was used in expert interviews to gather information on the barriers to the materialisation of four abatement options in the Netherlands: Insulation, Carbon Capture Storage, Biofuels and Geothermal energy. The potential of these options for the Netherlands is relevant, although no acceleration of their realisation has taken place. The Y-factor proved to be a suitable research method to systematically research materialisation barriers from a system’s perspective as it included barriers related to costs and financing, multi-actor complexity, physical interdependencies and behaviour. The supporting stories of the barrier scores and links between barriers revealed the complexity of materialisation. Additional scoping of abatement options and clarification of barrier definitions can increase informative value of the supporting stories and increase the scoring quality. Researching abatement options when using a system’s perspective can improve the understanding of barriers to CO2 abatement.

Improving energy efficiency is marked as one of the key strategies to tackle global warming. Therefore policy makers try to design policies that steer our society towards a more energy efficient way of living. An important target for policy makers is the residential sector. Households use fuel and electricity, to power all sorts of residential technologies and appliances. However, they often do not adopt the most energy efficient technologies available to them. There is no simple answer as to why households do not adopt energy efficiency sufficiently. This is because the adoption of energy efficient technologies can be described as a complex adaptive system. The decision making of households is dependent on complex attitudes, preferences, perceptions and needs. Nevertheless, policy makers try to influence the adoption decisions of households through the use of multiple policy measures. Academics have developed an interest in the potential effects of intermediary parties on the adoption of energy efficient technologies by households. We can specify an intermediary as: a party that is necessary for the adoption of energy efficient technologies, but cannot accomplish energy efficiency by itself. In this thesis project policies that include the role of the intermediary in residential technology adoption are compared to other energy efficiency policies by means of an agent-based model. Recommendations on future agent-based models on the role of the intermediary and efficiency policies are made.

By analysing existing agent-based model (ABM) charging behaviour formalisations, this study evaluates the impact of a new static band-width network tariff structure on charging demand. Additionally, this approach enables a comparative study to reflect upon the susceptibility and value of the ABM method. Based on the simulation results, this study shows how steering EV charging behaviour by implementing a static bandwidth tariff structure will lead to lower demand peaks. However, the results also show how the increased simultaneity among charging sessions are expected to mit-igate the decreasing impact of the intervention. Furthermore, this study signifies how interpreting and scoping of a complex system can lead to significantly different model dynamics.

The national government of the Netherlands has mandated heat transition to the municipality as part of the global execution of climate agreement. However, despite many guides has been offered, municipal heat-transition policy is a tricky matter that the municipal policy-makers struggle to grasp. To support the systematic municipal heat transition policy-making process, we propose the usage of a Decision Support System (DSS) that serves the whole cycle of policy-making. However, there is a knowledge gap of an integrated DSS framework with both policy target profiling and policy system (policy cycle) that is a context-specific for the municipal heat transition policy-making in the Netherlands. Therefore, this thesis study was conducted to answer the research question: "what decision support system framework can systematically assist municipalities in the heat transition policy-making process?" To answer this question, a refined Heat Transition Policy Making (HeTPoM) DSS framework is made based on a functional visual design method to cater to the needs of DSS creation in the context of municipal heat transition policy-making in the Netherlands. The refined HeTPoM DSS framework is designed to support policy-makers to create a systematic municipal heat transition policy by supporting the process of policy design, assessment, and evaluation. Furthermore, the refined HeTPoM DSS framework can potentially be used as a base to create a computerised municipal heat transition policy-making DSS. This then leads into two types of future research recommendations that need to be done in series. First is the HeTPoM DSS framework components mechanism study (i.e. the interaction between municipal heat transition influencing factors and the policy-making process). Which then can be followed by digital architecture of the DSS system to support the development of municipal heat transition DSS.

Margot Wallstrom once argued that there can be no sustainable development without sustainable energy development. Despite several organizations already recognized that (rural) electrification leads to socio-economic developments and improvements related to health, productivity and education in 2013, 1.2 billion people – half of which in Africa – still remain un-electrified. To address the problem, this qualitative research analyzes data through the so-called Gioia method previously collected utilizing Participatory Rural Appraisal tools (PRA) in the context of Kenyan BoP community. The final aim of the study than is to answer to the following research question: Which factors positively affect the diffusion of frugal innovation at the BoP? In view of this, the study tries to overcome the traditional lack of information regarding BoP consumers’ preferences through the case of a social enterprise diffusing frugal innovation in the Sub-Saharan Africa BoP energy market.