“How can port resilience in archipelagos be evaluated—and what lessons emerge?” This dissertation develops a novel method to evaluate port resilience by integrating the existing theories with real-world challenges. Through a multiphase mixed-method approach, three case studies were carried out: Pantoloan Port (2018 Sulawesi Earthquake & Tsunami), Seba Port (2021 Cyclone Seroja), and Mamuju Port (2021 Majene Earthquake). The results provide actionable recommendations to enhance port infrastructure, operations, and recovery—demonstrating the method’s usefulness for policymakers and port operators in vulnerable archipelago regions.

Due to the increasing trade of bulk cargo and the growing attention to efficiency in all steps of a supply chain that continuously becomes more complex, a thorough understanding of dry bulk terminal operations is crucial. While research on dry bulk terminal operations exists, it remains scarcely comprehensive. Existing studies primarily focus either on the storage space allocation problem, often in conjunction with berth allocation or on the simulation of the system, usually built for a specific case study. Furthermore, environmental factors have received limited attention in this context. Incorporating environmental considerations into this topic is crucial for sustainable and efficient terminal operations, especially when dealing with such a vastly spread sector as bulk cargo handling.
This work proposes a generic simulation model for grain terminals featuring a storage space allocation heuristic and the option to include cold ironing operations and unloading operations involving wind-assisted carriers. The generic character of the model is obtained by understanding what processes are common to different terminals and to what minimal level of detail they have to be modelled in order to obtain reliable simulation results. Moreover, the model gains further versatility due to its parametric and period-based character. These two features enable users to effortlessly conduct reliable simulations throughout all stages of design or revamping projects. This includes using information typically available at the start of a project as well as more detailed data that becomes available in later phases. The model includes the possibility to visualise and investigate the energy consumption of the system, crucial information to face present-day challenges such as the adaptation and enlargement of the electrical grid and the capacity estimation for the installation of new green energy production plants. The effectiveness of the model and its generic quality are validated with different study cases from real-world terminals. Additionally, in order to show the genericity of the model and its potential, this study features multiple experiments to investigate different aspects of terminal operational and energetic efficiency, involving the changes in operations caused by the introduction of shore power connections and wind-assisted carriers, a comparison of different unloading technologies, and a validation of the industrial common practices in the field.

The aim of this thesis project is to understand how policy instruments influence the adoption of wood-based building practices and to examine the effect of increased wood-based construction on circular practices. This study identifies key actors in the built environment, including housing associations, private owners, construction and demolition companies, and material suppliers. The behavior and relationships of these actors are analyzed. An agent-based model is developed to explore the impact of various policy instruments, such as carbon taxation, demolition notification, and knowledge sharing. The study revealed several findings. First, there is significant inertia among construction compa- nies and building owners towards adopting wood-based construction, primarily due to high initial investments and lack of familiarity. This results in a hefty premium being paid for wood-based construction before it becomes well established. It was discovered that a substantial subsidy on mass timber is essential. Additionally, significant taxation on reinforced concrete, such as through carbon taxation, helps overcome the inertia in the system. Another effective instrument is the sharing of wood-based construction knowledge among construction companies. Once wood-based construction is established, it becomes cost-competitive, reducing the need for continuous stimu- lation through policy instruments. With the establishment of wood-based construction, several effects on the circularity in the built environment and material usage have been identified. Despite a shift to wood-based construction, the demand for concrete remains significant. This underscores the importance of concrete recycling practices. The increase in wood content in construction requires enhanced mass-timber recycling practices. This study highlights the role of temporary material storage in facilitating circularity. It suggests the need for strategies to match material streams from demolition to construction. In summary, this thesis project demonstrates that a combination of policy instruments, especially carbon taxation and knowledge sharing, is crucial in transitioning to wood-based construction. The study highlights the need for continued attention to concrete use and recycling, especially when wood-based practices gain traction.

COVID-19 has had a great impact worldwide. One of its many effects was that offices had to be closed, which negatively affects the economy and people’s mental health. It is important that a safe way to keep offices open is found. Non-Pharmaceutical Interventions (NPIs) are an important way to do this. However, to pick the right NPIs for one’s office, knowledge is needed of the effects of the different NPIs within the given context. As of now, there are not many models that can provide a detailed estimation of COVID-19 spread and quantify the impact of NPIs in office spaces.
Therefore the existing model of the PedestrianDynamics - Virus Spread (PeDViS) of Duives et al. (2022) is chosen as the base model to be extended into a model that allows the comparison of the effects of NPIs in an office space.
The NPIs of which the effects are explored in this research are:
• All employees wear a mask while walking
• Increasing the rate of cleaning
• Increasing the ventilation rate from standard rate to the rate advised by government officials
• Allowing only 50 % of the employees to come into the office
• Allowing a maximum number of people in the meeting rooms, equal to 50 % of the capacity of the meeting rooms

A sensitivity analysis has been performed to learn more about the impact of some of the variables related to the meetings held in the office. Both the size and frequency of meetings were found to have some influence on the number of infections.
Next, policy analysis has been performed to learn more about the effectiveness of the NPIs, both individually and combined into policies. This led to conclusions on how large and consistent the effect of each of the NPIs was in reducing the number of infections in office contexts. The results also provided information on under what circumstances NPI could best be implemented and what NPI policies are recommended to be implemented to adhere to different levels of safety. Also, various limitations of this research were identified and recommendations for future work were given for part of them. The most important recommendations for future work are:
• Extending the model to include shared workspaces and the activity of having lunch.
•Testing the impact of more aspects relating to activity scheduling
•Gather data on people’s behaviour when the number of days they can come to the office is limited, to learn more about the inconsistent effect of this NPI.
•Test the impact of the NPIs in a context in which the initial infection risk is low.

The transition towards greener mobility will play a significant role in decarbonizing the economy. Hence, policy makers need tools that allow them to test alternatives towards that goal. This drive has led to the development of increasingly accurate transport models, with the latest generation being activity-based transport demand models. These models, however, are hard to build, as they are very data-intensive and complex, therefore in this research project a methodology is conceived which attempts to use readily available data from the Dutch travel demand survey (ODiN) and open source software such as ActivitySim, originally developed as a package in Python to make activity-based models in the United States.

While a very time-intensive task, the data was able to be processed for use in ActivitySim. The data was mostly complete, but needed to be complemented with data from the \textcite{centraal_bureau_voor_de_statistiek_statline_2019}, and information about members of the household other than the survey respondent, and joint tours, is missing. The data, however, can be processed in a way that can be reapplied in the future, which lowers the barrier to develop a model with Dutch survey data.

Choice is modeled as logit discrete choice models, and the estimation of the parameters required is facilitated by ActivitySim, which has built-in functionality to support it, and with an integrated workflow the model parameters can be estimated with little effort. With this procedure, and choosing workplace and school locations in advance, a good degree of accuracy was achieved, but it was shown that the sampling method used to deal with the very large choice set introduced significant bias to the model output, as observed in the travel distances, which were shorter in the simulated output than in the observations in the survey data.

While ActivitySim has a sampling methodology to deal with large choice sets, an alternative method, Stratified Importance Sampling with activity spaces, is implemented based on the survey data, from where the sample is determined using the travel distances observed and which produces more accurate outputs when compared to the default sampling.

The result of this research is a framework to easily develop and estimate an activity-based transport demand model that is able to provide insights on the travel demand, and especially on how to influence individual choice behavior, which can facilitate the procurement of quality analysis for decision support in the arena of sustainable mobility, hopefully helping accelerate the mobility transition.

It was concluded that using ODiN data and ActivitySim presented as advantages an easy and replicable formulation, and the availability of data that can be used for sustainable mobility policy analysis; yet, this formulation fails to account for household interactions, something that activity-based models often promise to do, and the documentation provided by ActivitySim while extensive is still inadequate in some regards to understand how to process the data.

The resulting model is, however, highly accurate, despite needing some considerations and improvements. The model needs to sample destination choice alternatives, because otherwise its big size would bring the model to a halt, and it does so using a sampling method that is programmed into ActivitySim. This method was shown to introduce bias to the simulation output even if the choice model was properly estimated, and hence an alternative sampling method based on Stratified Importance Sampling was implemented, and the model output greatly improved as a result. Hence, we conclude that it is possible to obtain highly accurate and efficient activity-based models using available data such as ODiN and open source software such as ActivitySim.

It is argued that a formulation like this can be highly beneficial to the evaluation of sustainable mobility policies, as it lowers the barrier to obtain the accurate and detailed outputs that other models cannot produce, and it provides accurate destination choices that will then inform other submodels that are necessary to evaluate sustainability impact, such as mode choice, travel distance and travel time to evaluate emissions.

We continue by discussing the limitations stemming from the available data and the lack of information for other household members and joint travel, the trend-breaking nature of the COVID-19 pandemic and is impact on mobility in further years, and the possible untested bias of the newly proposed sampling method; and giving recommendations on how to effectively use of a model developed with this framework.

Finally, further research is proposed regarding remote work, the improvement of the choice models, and on the sampling method used.

Illicit supply chain networks are not well mapped. Regulators do not know how goods flow from supplier to retailer. There is uncertainty about whom is involved, where goods originate from, and what quantities are being shipped. Simulation models are effective tools to find measures against the distribution of illicit goods such as personal protective equipment. However, simulation models often solely handle uncertainty through the variation of parameters. Structural uncertainty, which is uncertainty in the structure of the model, is often neglected. This study focuses on accounting efficaciously for structural
uncertainty in supply chain simulation models using model-driven exploratory modelling.

Model composability, which is a specific form of model driven exploratory modelling, is used in this study. The methodology is applied to a supply chain of illicit personal protective equipment. Using a model composer, many plausible models are generated of this supply chain. A model composer works by coupling model components in different configurations, while complying to preset constraints. Model components are submodels of a supply chain actors, for example, a retailer. Constraints help to restrict the way the model components can be coupled, making sure that every model generated by the model composer is plausible.

A ground truth is established to test the model composer on its efficacy to account for structural uncertainty. A ground truth is a simulation model of an illicit supply chain that functions as a benchmark. Five sets of 100 models are generated by the model composer to estimate the ground truth. Each set of models is generated with a different set of constraints. A constraint set consists of elements such as the maximum number of suppliers, the locations of supply chain actors, and the maximum number of customers of a supplier. These sets reflect different perspectives on an illicit supply chain.

Results show that structural uncertainty can result in significantly different simulation outcomes. The time in system, the production time, and the international transport time depend the most on changes in the constraints of the model composer. The time in system, the production time, and the international transport time of the models generated by the model composer are significantly different from the ground truth. The distributions of these outcomes have a different shape and have a wider range of possible values. Therefore, this study shows that model composability, a specific form of model-driven exploratory modelling, is efficacious in accounting for structural uncertainty in supply chain simulation models.

In the future, the methodology shown in this study can be used to model structural uncertainty in other fields such as water pipes networks, gas pipes networks, and telecom networks. Furthermore, the methodology can be used to identify robust measures to tackle the problem of illicit supply chains. Another recommendation is to use model composability for the individual components of the model. For example, a component such as a retailer can be build from several components: a cash register, a shelf, and a distribution area.

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.

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

Modern societies are heavily relied on efficient transportation systems for mobilizing people and goods. These systems are mainly constituted by road traffic networks. Currently, traffic demand is immense and perpetually increasing with unprecedented rates that traffic congestion has become an imminent subsequent. Over time, all this human activity that has established the status-quo of modern societies has been negatively influenced by climate change. Climate deviations are prominent in urban environments with a higher frequency and elongated time scales. Therefore, road traffic systems jeopardizing their robustness, and their resilience is at stake. A fundamental component in road traffic systems is the human factor. Nevertheless, human factors, to the contribution in traffic, are largely neglected. Some other times we consider that humans act rationally. Consequently, engineers seek answers to questions of how to incorporate the human factor into the system to explain the behaviour of human drivers under adverse weather conditions. In this contribution, an exploratory simulation study was used to put into perspective the derived conceptual frameworks and assess their performance in terms of efficiency and safety. Various psycho-cognitive mechanisms were utilized to address the human factor and rationally connected with the vehicle motion to reproduce the traffic phenomena that we observe under the conditions of rain and fog.

Since the reform and opening up, Chinese economy has developed rapidly, and people's living standards have been greatly improved. Chinese GDP in 2019 is 270 times of that in 1978 and per capita GDP in 2019 is 180 times of that in 1978. The income has increased a lot during the past 40 years, especially in metropolis like Beijing, Shanghai, Guangzhou and Shenzhen. These cities have attracted a lot of investment, providing a large amount of job opportunities, so the population of these cities expands rapidly as well as the scale of the cities. The number of civil automobile in 2019 is almost 5 times of that in 2000 and the travel distance is almost 3 times of that in 2000. Therefore, traffic congestion has become more and more serious in recent years. It takes more than twice as much time to travel during rush hours. The research object is to find out measures to control traffic congestion in Chinese cities.
In this paper, system dynamic is applied to study the transportation system which is also known as the policy laboratory. According to system dynamics, it is important to understand that the system is dynamic and the change of one variable can influence the performance of the whole system. In this research, system dynamics is used to establish a simplified model to reflect the real transportation system. By adjusting the values of the variables, the effect of different policies can be simulated, and the efficiency of the policy can be evaluated.
Firstly, this paper analyzes the traffic development process at home and abroad, summarizes the development characteristics of each stage, and summarizes different measures of congestion control. Secondly, the paper analyzes the traffic operation characteristics of Chinese cities and the causes of road network congestion. Traffic congestion in China mainly includes external factors and internal factors. The external factors refer to the growth of traffic demand caused by economic development, population growth and urban expansion. The internal factors refer to the poor management of the transportation system. Thirdly, based on the causality relationship between different factors, the system dynamic model with virous policies is established. Due to the fact that most of the developed cities located in the southeast China and Shanghai is the largest city in the south, it is selected to be the example of this research. In the end, specific measures are put forward based on the simulation result of the model.

The Airport Coordination Netherlands monitors the conformity of airlines to the allocated slots in cooperation with the relevant authorities. ACNL discovered the non-conformity behaviour of slot usage in Schiphol Airport of the Netherlands, and among nine types of misuses, this paper will discuss the one type of misuses: a flight operated at a significantly different time from the allocated slot. The slot coordination is consist of 5 procedure, the slot allocation, slot monitoring, discussion, enforcement and sanction. Using the exploratory data analysis, the research will explore the different types of behaviour from 5 selected airports, and suggest ways of continuing the monitoring to contribute to the slot monitoring and discussion process.

The pressure on healthcare systems is increasing all over the world. With an ageing world population, the costs for healthcare and the shortages in medical staff are continually increasing. In the Netherlands, out-of-hours general practitioner departments, or ‘huisartsenposten’, suffer from this increasing pressure in their telephone triage systems: the departments are crowded and the staff capacity is too low to adequately handle the amount of patients. This often leads to long waiting times on the phone for patients in need of potentially urgent medical care and to high pressure work environments for staff. There is no insight into when and why it is crowded, how high waiting times emerge from this, and how changes can be made in the departments internally and beyond to reduce this problem. In this thesis, research is presented that addresses the practical and scientific lack of knowledge of the factors that influence the out-of-hours departments and that identifies how waiting times can be reduced. The results of this thesis focus on the identification of the factors that influence the demand for healthcare and the service times of people at the out-of-hours departments, and they focus on the practical implications for reducing waiting times at these departments. Based on extensive data analysis of two out-of-hours departments in the Netherlands, it was found that temporal factors such as season, part of the week, day of the week and hour of the day, but also the weather conditions and the urgency of the problem of the patient have an impact on demand for healthcare and on service times at these departments. These factors determine how busy it will be and whether or not waiting times will emerge. With the knowledge of these factors, a discrete event simulation model was implemented to identify what system changes are necessary to reduce waiting times at these out-of-hours departments. It was found that there are several quick-win interventions that can help reduce waiting times: shifting of patients from peak demand, implementation of overlapping work shifts for staff and automatic retrieval of patient information when they are waiting in the queue on the phone. There are also some long term interventions, more focused on behavior change of people, that can be implemented: increased accessibility and understanding of the primary healthcare system, a small (dis)incentive for out-of-hours care, separate telephone lines for home care and implementation of working from home for staff.

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

Modelling and simulation is at the heart of Digital Twin technology, which is revolutionising many industries. Organisations have access to legacy models that are too complex to maintain, preventing them from operationalising complex systems like Digital Twin. By creating an automated tool for model decomposition, it is possible to breathe life into complex models by extracting their embedded functionality as managable components. This thesis presents NewMODE as a network theory approach to model decomposition; a novel methodology that aims to automate the tedious task of model decomposition. Important contributions include the network theory metamodel specification, the decomposition criteria and the adaption of the Girvan Newman algorithm to identify components of the model. NewMODE has been implemented for models in developed in LSAT (Logistics Specification and Analysis Tool). In partnership with TNO and their Embedded Systems Innovation (ESI) group, NewMODE has been evaluated quantitatively and qualitatively with promising results to aid model developers in model decomposition.

To tackle increasing health care costs and increase quality of health care the prevention of unnecessary admission by direct referral for elderly with a social indication from the hospital to other care institutes is perceived successful by stakeholders. No method to assess the effectiveness was present in literature. A six-step assessment method is designed based on literature and tested and reflected by an application to the Zorgtransferium-process of the Albert Schweitzer Hospital. The six steps are the the Description of the object of study, the Stakeholder selection, the Indicator collection, the Indicator selection, the Data collection and the Data analysis. The Zorgtransferium-process is effective on 'Availability of hospital beds' (374 occupant days/year), 'Referral Distance'(average:8.7 km, min: 0 km, max: 19 km) and 'Referral Time' (average: 22.6 hours, min: 2 hours, max: 78 hours). The assessment method creates overarching insight for the stakeholders on the process and makes future assessment of effectiveness possible. In the future more research is needed to create a benchmark or generally accepted norms to place the results on effectiveness in broader context, the process should be expanded to other patient groups and the evidence-based management approach used in this research should be applied to other pilot projects in hospitals.

Service reliability is one of the most important performance measures to public transport users. Detecting disruptions helps to measure service reliability, which can be used by public transport operators to improve this reliability. In this thesis, a methodology is described to automatically detect disruptions offline, using smart card data. The day-to-day regularity of delays is investigated using hierarchical clustering on a training set, to distinguish between regular and irregular delays. The clustering result is used to create a probabilistic classifier. This classifier is applied to the test set to find days that do not correspond to a regular pattern: irregular days. After that, disruptions are detected within the irregular days. The outcomes of this study can be applied in multiple ways. Locations where disruptions have occurred can be found and the related passenger delay can be calculated. This can help public transport operators to prioritise which locations to focus on to reduce passenger delays. Furthermore, not only public transport networks, but also other networks can benefit from the outcomes of this study. Speed data of road networks could be used to find disruptions that are caused by accidents, instead of regular traffic jams. On top of that, this study could be used as a step towards real-time disruption detection, for both public transport and road networks.

China is undergoing massive changes in order to tackle their carbon dioxide emissions problem. Aside from the problem of climate change, it is facing another problem – that of air pollution. Use of dirty fuels by residential households such as biomass and coal cause emissions harmful for the environment, as well as to the health of citizens. In order to reduce this, local governments are tasked with inducing a change in household fuel consumption from dirty fuels (such as biomass and coal) to ones that are cleaner and more efficient, such as natural gas or electricity. The ‘lower-tiered’ cities are often left to their own devices in how to implement suitable policies in order to reach these targets. As a result, the policies that are introduced often ensure targets are reached, but also leave citizens without any fuel alternatives, or forces residents to resort to other unhealthy fuels. This research investigates how lower-tier prefectures can stimulate their residents to shift daily energy fuel demand from traditional fuel use to cleaner and more efficient fuels, while preventing the population from unwillingly reducing their total energy demand. The research was performed through a case-study research in the prefecture city of Jingmen in Hubei province. Energy consumption demand behaviour was investigated using a system dynamics model through mechanisms of citizen reaction to energy price level, convenience of fuel consumption, knowledge about clean and efficient energy use and willingness to making a conscious change in consumption. The research finds that the urban citizens are concerned about the effects of fuel consumption on their personal health, while the rural population shows larger concern for the price of fuel and convenience of consumption. A ban on coal will best reduce emissions but it should be implemented in combination with a supportive policy in order to prevent citizens from unwillingly reducing energy demand. Other policies, such as providing an energy subsidy will be less effective and lead to higher levels of uncertainty.