Currently, the number of refugees is increasing, while the effects of climate events are worsening. Given this context, it is becoming increasingly important to understand the potential relationships between refugee movements and climate exposure, particularly at the origin and destination locations of refugees. This understanding is crucial for predicting future movements of climate-related refugees and identifying effective climate adaptation measures at both origin and destination locations.

Research on the global relationships between refugee movements and climate exposure is lacking in the current literature, especially in terms of data-driven, analytical approaches. This research addresses this gap.

The main research question of this thesis is: How is climate exposure associated with global refugee movements? To answer this, a literature review and data analytics are employed. The literature review identifies climate-related hazards that serve as inputs for the data analysis. The hazards considered in this study include droughts, sea-level rise, coastal flooding, riverine flooding, and cyclones. Various types of (possible) associations are examined using correlation analyses, regression analyses, spatial cluster identifications, and bivariate choropleth maps. All analyses consider changes over the years, specifically from 2003 to 2022.

As one of the results, this research presents an equation for relatively accurate predictions of refugees fleeing from countries in recent years, based on climate indicators while also incorporating social factors.

Moreover, the results of the study indicate that sea-level rise and riverine flooding are significantly associated with refugees fleeing from a country. Future research should explore whether this relationship could potentially be causal.

Additionally, the study identifies hotspots for different climatic conditions as well as for refugee movements (both fleeing to and from countries). The hotspots for climatic indicators and refugee movements align relatively well. Generally, climate exposure and large refugee movements are concentrated in Central-East Africa, the Middle East, and South-East Asia. These hotspots have not changed significantly over the last 20 years.

Lastly, the thesis compares top origin-destination locations of refugees at both generic (statistical) and individual (visual) levels. While statistical differences between top origin and destination locations are sometimes inconclusive, individual comparisons of countries within the same regions provide key insights into the differences in climatic exposure for refugees.

More effective climate adaptation measures can be implemented at refugee destination locations by tailoring them to specific climatic exposures, taking into account the conditions at their origin locations. Specific investments regarding adaptation measures have been identified for particular regions and countries, and these are recommended to policymakers.

The procedures and tests developed in this research can be applied to similar studies using different or newer climate and refugee data.

The increasing frequency of climate-related disasters poses significant threats to coastal communities, disrupting not only physical infrastructure but also the social, economic, and environmental systems that underpin daily life. Community resilience, defined as the ability to withstand, adapt, and recover from these disruptions, is crucial for ensuring the long-term sustainability of these areas. However, existing resilience frameworks often fall short as they lack cross-sectoral integration, do not account for the interconnectedness of infrastructure systems, and fail to adequately incorporate climate risks or adapt to local cultural and environmental contexts. Moreover, these frameworks frequently overlook the diverse perspectives within communities.

To address these gaps, the NERMI CCR (Coastal Community Resilience) Framework combines quantitative methods such as Principal Component Analysis (PCA) with expert consultations to refine 134 key resilience indicators across five dimensions: Social, Infrastructure, Environmental, Organizational, and Economic. This approach provides decision-makers with 110 fixed indicators and 24 context-specific indicators, ensuring flexibility and promoting an integrated, system-of-systems view of resilience planning across diverse coastal settings.

Natural disasters pose significant threats not only to the environment and infrastructure but
also to human lives. Therefore, effective disaster risk management must account for physical
and social vulnerabilities. This research focuses on developing a comprehensive tool for assessing social vulnerability to enhance disaster risk management strategies. Social vulnerability is a complex concept that includes various dimensions, such as socio-economic
and cultural factors. Recognising the multifaceted nature of vulnerability, this study aims to
create a multidimensional index that equally represents each dimension of social vulnerability. By incorporating intersectionality theory, the research acknowledges that vulnerability is compounded and varies across different population groups. The proposed multidimensional index method introduces new calculation tools to balance the weights of all social dimensions in the composite index. Utilising socio-economic data from Indonesia, this study calculates the social vulnerability index and compares it with the existing Social Vulnerability Index (SoVI) to identify deviations and improvements. To validate the applicability of this alternative calculation, the study applies the new index to real disaster scenarios, specifically focusing on flood events in Indonesia. The research findings demonstrate that the multidimensional index effectively measures all aspects of social vulnerability in a proportional manner. The research also identified several significant policy implications, including the prioritization of policies, targeted policy development, and customized policies for disaster risk management.

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

Cash and Voucher Assistance (CVA), a type of humanitarian aid consisting of giving money instead of products, is being used more frequently because of its effectiveness and efficiency in helping people in need (Cash Learning Partnership, 2020b). The debate on using CVA is currently focusing on improving the quality by better incorporating ’voices’ (needs and preferences) of recipients and by enhancing targeting. In targeting it is a major challenge to quickly identify the individuals and families with the biggest needs, given the lack of data (Aiken et al., 2021). Research on ways of measuring impact on and satisfaction of recipients combined with research on demographic and behavioural characteristics of recipients could lead to deeper insights in recipients of trackable CVA modalities (evouchers and ecash). This research uses the marketing literature on customer segmentation combined with machine learning algorithms to come up with an innovative new approach of categorizing recipients of evouchers, using the case of a Red Cross project on Sint Maarten. The main research question is: How can recipients of cash and voucher assistance be categorized using the field of consumer segmentation by using machine learning methods? The objective of this research is to come up with new methods to better understand recipients of CVA. Theories on customer segmentation pointed to the use of data-driven clustering methods to categorize consumers. Combined with a framework of recency, frequency and monetary aspects, recipients of evouchers could be categorized effectively. A required addition to this clustering method is to use a dimension reduction technique to avoid the negative consequence of the curse of dimensionality. Therefore, a two-step approach of dimension reduction and clustering has been applied in this research. It has been found in this research that a factor-cluster approach can lead to insightful clusters using geo-demographic data and behaviour data. Factor analysis has been used to reduce the dimensions while the k-prototype algorithm has been used to cluster into five distinct groups of recipients. The geo-demographic variables that were the most determining in characterizing distinct clusters consisted of: the age of the main beneficiary, the different household compositions and of a constructed factor ’big families and big receivers’. The most distinguishable variables on behaviour were: the number of supermarket visits (frequency), the time between the first voucher was received and the first transaction (recency) and the variables on the amount of money that was spent with the vouchers (monetary). To be able include the ’voice’ of recipients (needs and preferences), a connection between the registra tion data, behavioural data and survey data is needed. In this research only an exploratory connection could be established, due to the lack of a common identifier between the survey data and the other datasets. However, one crucial finding of this research is that it seems like the combination of these data sources can give meaningful insights in the needs, preferences and behaviour of households of Sint Maarten. With these insights specific clusters can be targeted for additional assistance, based on their needs. Recommendations for future studies include studying the validity of the found cluster results with different validation indices on cohesion and compactness, and by using simulations to determine the cluster stability. Before this factor-cluster approach can be deployed in CVA projects, more research on the treatment of limitations of this approach needs to be conducted. This is critical in communi cating the conditions and constraints of this model to humanitarian aid workers in the field. Another recommendation is to improve the design of surveys to measure the needs of recipients. For insightful factor-cluster results on the needs of recipients, survey data should be linked to geo-demographic and behaviour data. More research on including clusters in retargeting methods using feedback loops have a large potential in minimizing targeting errors and more effectively meeting the needs of recipients. With this research, the humanitarian sector can benefit from new ways to understand the needs of the most vulnerable in need. Decision-makers should build upon the feedback of recipients and move towards a new era of humanitarian assistance.

Land-use change models are often used to explore future land-use. Currently, most land-use change models run a small number of predetermined scenarios. To better address the multidimensional nature of uncertainty about the future, previous studies have argued for covering a wider range of the uncertainty space than is possible with existing scenario approaches. One way to approach this, is by using exploratory modelling. Instead of running the model on a small number of pre-defined scenarios, one uses sampling techniques over a plausible range of the uncertainty space to generate large-scale simulation experiments. With respect to land-use change models, this results in a large number of future land-use maps. To better make sense of the resulting maps, the next step necessitates the identification of plausible distinctive land-use patterns through clustering algorithms. Previous studies regarding quantification of land-use maps (dis)similarity focus only on comparing maps on a one-to-one basis or in small numbers for validation and calibration purposes. In this study the use of different map of similarity metrics on the resulting clusters of land-use patterns is systematically investigated. Specifically, the implications of using various cell-by-cell similarity metrics and landscape structure similarity metrics to cluster the resulting land-use maps are tested. The Land Use Scanner model is used for this purpose. It was found that the choice of (dis)similarity metric plays a significant role in the formed clusters of maps. If exploratory modelling is to be applied to land-use change models, it is thus important that great care is taken in the selection of the proper clustering algorithm.

During the battle against COVID-19, sudden-onset disasters keep happening. These two consecutive events are different in nature and sometimes ask for counter-productive policy interventions: where the epidemic can be contained by ensuring social distancing practices, reducing the number of contacts, and offering hygienic conditions, the response to a natural hazard requires evacuation, which often leads to overcrowded shelters with a questionable hygiene. An exploratory agent-based model is constructed where the dynamics of these socio-technical systems are integrated to find high-level emergent behaviour over time during the response phase of a sudden-onset disaster. The study describes a stylistic agent-based model that combines three socio-technical systems, their interactions and system behaviour to find general trends and interdependencies. The three sub systems comprise of a COVID-19 component, based on the SEIR approach, a livelihood component, which mimics a microeconomic marketing mechanism and accounts for among others difference in occupation, and a hazard component, which is responsible for the evacuation process. The following research question is answered: What robust policy interventions can be identified that balance livelihood of rural communities and their exposure to COVID-19 during the response to a sudden-onset disaster in developing countries? The goal is to find robust policy interventions that can be executed by either local governments or humanitarian organizations and are suitable for poor rural communities in developing countries. Four policy interventions were compared: cash transfers, an awareness campaign, increasing the number of shelters, and varying the moment of imposing a lockdown. With the use of Exploratory Modelling and Analysis, an experimental design was constructed to identify uncertainties and model behaviour. Results show that using direct and unconditional cash transfers in combination with increased awareness have most beneficial effects for the average livelihood and also positively influence the COVID-19 trajectory. Increasing awareness shows best results for containing COVID-19, but is dependent on context factors such as regular testing and implementation before the the virus spirals out of control. Awareness also negatively influences the average livelihood. This research is a first step in modelling compound risk of COVID-19 and sudden-onset disasters. The modular way of building makes this research useful not only for searching trade-offs in developing countries, but can be used in other contexts as well.

Situation: A large portion of the beneficiaries of humanitarian aid have little to no proof of their identity, this is especially the case in African countries, in which humanitarian intervention is common. In order to facilitate aid to these individuals, humanitarian organizations (HOs) leverage their on-the-ground capacity to create identity profiles and risk assessments of these people. However, in-kind aid is being increasingly replaced by Cash Transfer Programs (CTPs). In CTPs, beneficiaries are provided with funding to self-procure their necessities. Complication: For efficient CTPs, these humanitarian identities need to be accepted beyond the boundaries of humanitarian aid. However, by relying on traditional identity management systems, HOs expose the beneficiaries to security and function creep risks. In order to share beneficiary identity information in a more responsible way, HOs have started to leverage Self-Sovereign Identity (SSI) systems. These SSI systems need to be scaled up to a more foundational nature in order to facilitate private-sector services. For this a collaboration with national public- and private stakeholders is required. In order to realize that, there is a need for a process design. Which among other things ensures support from crucial stakeholders. Approach: This study used a Design Science Research inspired approach, combined with a Systems Engineering perspective to explicate the problem, define support driving and constraining circumstances and conditions, generate principles for the humanitarian sector to create support driving circumstances and conditions in Kenya and validate these principles using the input of industry experts. This research approach had a focus of practical insights over theoretical insights. Results: A set of five validated support nurturing principles were established with which humanitarian organizations can nurture support for a more foundational humanitarian SSI system in Kenya. Additionally, a framework has been composed with which local circumstances and conditions in a country can be assessed. Next steps: Further research should focus on establishing a more complete process design for a collaboration process, which also deals with participation of stakeholders, structures commitment and defines process rules for different phases of the process. Additionally, further research should explore the capacity and willingness of beneficiaries to control their own identity. And finally, the effect of international innovation initiation on the willingness of national stakeholders should be explored.

Development Finance Institutions have been established to provide finance for long-term economic development in low and middle-income countries. However, these investments are exposed to climate-related risks. This means that Development Finance Institutions need to explore climate adaptation interventions, particularly in view of the fact that developing countries themselves have few resources for climate adaptation. This thesis aims to establish how, using an agent-based model, Development Finance Institutions can achieve long term investments by financing climate adaptation, while creating societal benefits and financial returns. This implies positively contributing to society, for example by creating jobs, generating a stable income, and supporting the resilience of households, while securing profitability for development banks. An investment by the Dutch Development Bank FMO in the cocoa sector in Ghana serves as a case study. Three main steps are followed in the study: (1) translating global climate scenarios at the local level; (2) capturing local behavior in a bottom-up model because understanding the specificity of the local context and individuals’ response is key to really make an impact; and (3) testing climate adaptation interventions. Downscaled climate scenarios are used as input in an agent-based model capturing the behavior of Ghanaian cocoa smallholders. Experimenting with climate adaptation shows that Development Finance Institutions have to make trade-offs in terms of priority, timing, and activity. This step-by-step approach could encourage DFIs to capture the impact of climate change and the dynamics at the local level, and to design tailored interventions. It is also a way to take a holistic perspective and look at the broader picture of economic and societal impacts of climate change, and the response across the whole value chain.

Most people of Malawi are dependent on rainfed agriculture for their livelihoods. This leaves them vulnerable to drought and changing rainfall patterns due to climate change. Over time, farmers have adopted local strategies and knowledge that help reducing the overall vulnerability to climate variability shocks. One other option to increase the resilience of rainfed farmers to drought, is providing forecast information on the upcoming rainfall season. Forecast information has the potential to inform farmers in their decisions surrounding agricultural strategies. However, significant challenges remain in the provision of forecast information. Often, the forecast information is not tailored to farmers, resulting in limited uptake of forecast information into their agricultural decision-making. Therefore, this study explores whether drought forecast information can be linked to existing farmers strategies and local knowledge on predicting future rainfall patterns. During a period of three months in Malawi, participatory research approaches are used to create an understanding of what requirements drought forecast information should meet to effectively inform farmers in their decision-making. Consequently, a sequential threshold model was established that relates annually monitored meteorological indicators before the rainy season, to the occurrence of dry conditions during the season. Dry conditions were expressed in the drought indicators that farmers require for their agricultural decision-making. Additionally, using interviews among stakeholders and a visualisation of the current information flow, further insights on the current drought information system were developed. Although farmers have their own strategies and timing of decision-making, this research has generalized some of the opinions and strategies to develop the ‘requirements’ which a contextualized forecast should meet. In August farmers require a prediction of the onset of the rainy season, typically starting mid-November. In addition, an update on the timing of the onset of rains is required in beginning of November. An overall indication of the ‘dryness’ of the rainy season is required in September. Here, ‘dryness’ is characterized by the number of dry spells, a composite ‘drought index’ of associated rainfall variables by the farmers. The forecast should be on a scale that is locally relevant (EPA level). This research consequently established a forecasting model, based on meteorological variables from local knowledge which can complement the forecast variables from the DCCMS. The results of forecast verification show that meteorological indicators based on local knowledge have a predictive value for forecasting drought indicators. Subsequently, skill analysis of forecasting incorporating all the above dimensions shows that the accuracy of the forecast differs per location with an increased skill to the Southern locations. In addition, it is also location dependent whether the contribution of wind, temperature or ENSO indicators gives the most predictive value. The results show that a combination of all indicators have the best predictive value. In addition, the results show that local knowledge indicators have an increased predictive value in forecasting the locally relevant critical events in comparison to the currently used ENSO-related indicators by the DCCMS. Additional research is needed to further analyse certain aspects of this research, such as research on the robustness of the model used. Research on the risk farmers are willing to take in their respective decisions could act as another requirement the forecast skill should meet. This highlights the importance of having continuous feedback from the farmers, since farmers may experience adverse impacts from wrongly informed decisions. Despite these limitations, it is argued that the inclusion of local knowledge in the current drought information system of Malawi may improve the provision of forecast information for farmers and shows that it is possible to capture local knowledge in a technical approach. The findings have relevant implications for other stakeholders, such as humanitarian and meteorological organisations, that are implementing drought-risk reduction approaches and climate services.

This thesis aims to manage information exchange at the case company by designing an information exchange map using a business process model for the customer-to-customer process, together with structuring team meetings and creating ground rules for efficient information exchange.

The Damper Division of ZF Friedrichshafen AG (case company), Schweinfurt (SCW) has recently decided to move the production of
its high running parts to its facility in Pune, India. The project is facing typical Supply Chain Management challenges like delay in serial production and delivery schedules, lack of information exchange, ambiguity over roles and responsibilities, and redundant communication between the two facilities SCW and Pune, thus incurring a high transaction cost and low coordination. The literature review about information flows, business processes and process integration provided four principles that help to integrate the information flow between processes. These principles are – accessibility, transparency, granularity, and timeliness. The analysis pointed that there are information gaps and interrelationship issues
between the stakeholders of the project. The issues identified were the need for an information exchange map which can be relied on by all of the stakeholders, timely information exchange, defined responsibilities and roles, and efficient communication. Standardization of information exchange with the help information exchange map together with information sharing rules emerged out as the main conceptual design. This was followed by a participatory design phase where the design was developed together with the stakeholders of the project.

Based on the final design with information sharing rules (meeting structure and ground rules), a set of recommendations (including an implementation plan) were laid out to help the managers steer the final design to the best of their use. Currently, the case company is about to implement the design together with the meeting structure and ground rules. It was recommended that the future objective of the company should be to achieve a control tower for central data collection and using the data to generate insights.

The frequency and severity of natural disasters is increasing worldwide, leading to a growing number of people struggling to survive. While climate related natural disasters affect large portions of the world, communities who are already struggling to survive due to conflict, insecurity or poverty are hit the most. In fragile states, slowly unfolding natural disasters are getting more and more intertwined with conflict. In these areas, humanitarian and peacekeeping organizations have increasingly overlapping goals and scarce resources. Sharing information between humanitarian and peacekeeping organizations can improve the effectiveness and efficiency of both humanitarian response operations and peacekeeping missions, which may result in not only saving time and money but most importantly saving lives and reducing human suffering. Nevertheless, the process of information sharing between humanitarian and peacekeeping organizations is not common practice. This is a comprehensive study on the complexities of information sharing between humanitarian and peacekeeping organizations in fragile areas. It includes desk research, interviewing, modeling approaches and a qualitative case study on Mopti, Mali where the Red Cross Movement is actively fighting food insecurity and Dutch peacekeepers are contributing to the UN peacekeeping mission called MINUSMA.

In an emergency, humanitarian organisations share information to prevent redundant data collection and avoid gaps and overlap in the relief activities that they undertake. An analysis of hygiene kit distribution in the Bangladesh-Myanmar displacement crisis and consultation of both literature and humanitarian professionals led to the construction of a model on information diffusion in complex emergencies. This model proved to be able to evaluate strategies that have a level of complexity that could not be apprehended by existing models. Experimentation with this model leads to the conclusion that a locally sourced team, with an outward focused organisation that produces near real-time information products, is most effective in diffusing information.

During the next few decades, a significant increase in the use of intermittent renewable energy sources is expected in the Netherlands as well as a general increase in electricity consumption. Due to the increased demand as well as a more uncertain, volatile supply, substantial upgrades and redesign of the current Dutch electricity grids are needed. These upgrades will inevitably require large investments as the design and installation of electricity grids is costly. Additionally, the investments are lumpy and irreversible. It is therefore important that the investments will be made in such a way that the future grids will operate successfully, supplying consumers with the demanded electricity at sufficient quality with a low rate of interruptions. Recently, the Netherlands has been divided into 30 energy regions, which allows the Netherlands to work on its climate agreements both from a regional and from a national level. These regions will work on generation and consumption of electricity and heating as well as on the energy infrastructures needed to supply this energy. This aim of this research paper has been to create a method that can be used to design suitable electricity distribution networks for the energy regions in the Netherlands. One approach to designing electricity grid topologies is with the use of graph theory heuristics, which has shown to be a useful way of approaching the electricity design problem by discretising plots of land into a graph. The research paper has shown that by taking into account spatial constraints specific to the Dutch regions, more valid networks can be created. This further leads to increased implementability of the final networks in addition to a reduction in the possibility for unforeseen costs related to building on certain plots of land. The proposed method aims to minimise the investment costs of the future regional electricity distribution networks in the Netherlands, taking both cable lengths and capacities into account. A radiality constraint is applied, ensuring that the network is connected but does not contain any cycles. A flexible way of ensuring that the final networks do not overlap with unavailable land is thereafter applied and demonstrated. A heuristic method aiming to minimise network length is applied before assigning the required capacities to the network. An improvement procedure is performed in order to further reduce investment costs. The cost function is formulated as a non-linear function, incorporating the characteristic that savings can be made by combining lines in order to create a shorter, high-capacity network instead of a longer, low-capacity network. The proposed method has thereafter been verified with respect to the problem formulated and demonstrated using a case study on the energy region Goeree-Overflakkee. Experimental results have also been generated in order to assess the effectiveness of the method. In comparison to an alternative simultaneous topology and production optimisation, it has been found that the proposed method leads to a shorter final network that additionally leads to lower total investments costs.