Urban Building Energy Models (UBEMs) are emerging as a powerful tool for cities and regions seeking to make decisions on the best pathways for increasing the energy efficiency of their buildings. As model results are used to inform critical policy decisions, it is essential to understand and communicate the limits of inference of model results and how sensitive they are to changes in inputs. In the absence of standard datasets and protocols for model validation, Uncertainty Analysis and Sensitivity Analysis (UASA) procedures offer vital insights. However, there is no consensus on how UASA should be applied to bottom-up building physics-based UBEMs, nor on how different use cases might influence the choice of UASA approach. This study uses a systematic review of the literature (2009-2023) to explore the procedures which are applied and assess their appropriateness. We find a need for a more holistic view of uncertainty to be taken, and present a decision framework for selecting the most appropriate form of quantitative sensitivity analysis, based on model form, data provenance and use case. We also propose a number of approaches to improve the application of sensitivity analysis in UBEM studies, including the importance of undertaking a complementary assessment of information quality.

In 2020, owner-occupied housing accounted for 57% of the housing stock in the Netherlands. Homeowners are fully responsible for the implementation of energy retrofits. Moreover, the processes of energy retrofitting are complex and homeowners face problems such as finding financial support, reliable information and contractors. The complexity of implementing energy retrofits may discourage homeowners from continuing the process and achieving the expected benefits. Behavioural aspects and transaction costs (TC) are among the most important factors influencing consumer decision-making processes. Behavioural factors primarily illustrate a range of personal, contextual, and external factors that influence the decision-making process of homeowners. These include cognitive awareness and biases, attitudes and beliefs, experience and skills, homeowner characteristics, sociodemographic characteristics, property characteristics, and the behaviour of others. TC are any hidden costs that influence decision making but are not included in the direct physical costs of renovation services and products. This dissertation developed an integrated framework of behavioural factors and TC that impede the decision-making
process for energy retrofits. Key findings include (1) the significant importance of
behavioural factors and TC barriers. (2) the behavioural factors are particularly important in the early stages of energy retrofits and the TC barriers after the final decision. (3) the importance of behavioural factors and TC barriers differs according to the type of energy retrofit and non-energy retrofit. (4) Accounting for cognitive biases significantly improves the prediction of households' actual decisions about energy retrofits. This modelling is more accurate than the model that assumes households make rational decisions.

The building sector is responsible for more than one-third of global greenhouse gas (GHG) emissions. The Netherlands has set an ambitious target to reduce GHG emissions by 95% by 2050 compared to the 1990 baseline. Several factors, such as low retrofitting rates, lead to uncertainties in achieving these targets. In the residential sector, the energy retrofit rate of the owner-occupied homes is low. Homeowners encounter different types of barriers when deciding to make energy retrofits. The purpose of this study is to explore the policy implications of the main identified influencing factors and consequently the potential mismatch between current policy and the homeowners’ actual needs. We used semi-structured interviews and focus group meetings with experts from the largest cities in the Netherlands as the data collection methods. We identified the discrepancy between current policy and the actual needs of homeowners as follows: (a) less attention to the right message and the right messenger: policymakers cannot motivate the households using the word sustainability. Policymakers can convince homeowners to make energy retrofits through the improvement in quality of life, the expected cost savings, and the integration of energy retrofits into the maintenance of the home (message effect). Moreover, the trustworthiness and familiarity of the energy ambassador with the households are the main characteristics of these ambassadors (messenger effect). (b) the lack of integrated financial, informational and technical support: the main identified transaction cost barriers (non-monetary costs) are difficulties to inspire homeowners to carry out energy retrofits, lack of knowledge on how to start the energy retrofits, many steps in carrying out energy retrofits of old houses. More importantly, there is a lack of an active and accessible party in the market to reduce the financial, technical and informational barriers.

Retrofitting residential buildings can help mitigate the effects of climate change. Cognitive biases are systematic deviations from rationality in decision making and can lead to inaction, delay, and uncertain decisions. Understanding the cognitive biases involved in residential renovation decisions and developing interventions to overcome them can help increase residential renovation rates. Despite their importance, few studies have examined the impact of cognitive biases on energy retrofits. The question addressed in this study is: “Can accounting for cognitive biases improve the prediction of homeowners’ actual investment decisions, and how can the outcomes be used to recommend potential behavioural interventions?”. Expected Utility Theory (EUT) and Cumulative Prospect Theory (CPT) are compared to evaluate which model(s) more accurately describes actual decision-making behaviour regarding energy retrofits. The EUT assumes a rational decision maker. The CPT is a quantitative model that assumes a decision-maker operating under risk and uncertainty and subject to the cognitive biases of reference dependence, loss aversion, decreasing sensitivity, and probability weighting. The influences of cognitive biases on energy retrofit decisions can be quantified if the relative performance of CPT versus EUT is more accurate. The data for these analyses come from housing surveys conducted in the Netherlands in 2012 and 2018, which also collected data on energy modules. 2,784 and 2,878 homeowners were surveyed, respectively. The model is validated by estimating the coefficients of EUT and CPT and identifying the similarities and differences between the results of the two datasets. Before estimating the parameters, four household clusters are identified using grey relational analysis and the K-Means cluster. For the first time, the EUT and CPT parameters are estimated for four clusters and two energy retrofits, double glazing and insulation, using a genetic algorithm because the equations are nonlinear. The results confirm that CPT provides a better description of the actual decision behaviour than EUT using the two previously established initial values of Layard et al. (2008) and Häckel et al. (2017) as well as the parameters estimated by the genetic algorithm. In the latter case, CPT correctly predicts the decisions of 86% of homeowners to renovate their homes to be energy efficient or not. EUT, on the other hand, overestimates the number of decisions to renovate: it incorrectly predicts retrofit for 52% of homeowners who did not renovate for energy efficiency reasons. Using the estimated parameters of CPT, the cognitive biases of reference dependence, loss aversion, diminishing sensitivity, and probability weighting can be clearly seen for different target groups. The groups with the highest average incomes and house values had the highest loss risk aversion parameters. These households invested more in installing insulation and double glazing.

In 2018, the average number of occupants per dwelling is steadily decreasing, creating a demand for small, affordable housing. According to European Union energy targets, new small homes should be energy efficient. However, data clearly shows that energy efficient homes are mostly unaffordable and there is an urgent need to design and build small affordable zero energy homes. However, a sustainable business model for small affordable zero energy homes has not yet been developed in European countries. The Housing 4.0 Energy project explores the development of affordable zero energy homes in three countries: the Netherlands, Belgium and Ireland. This study explores the business models and potential for scaling up the five schemes (Ireland operates three schemes in different counties). The results of this study may be useful to practitioners, policy makers, and small families facing the problem of affordable zero energy homes. The Dutch scheme targets a market of self-builders of low-middle income households. In the Flemish scheme, non-profit social rental agencies provide the houses for low-income groups. In Ireland, local authorities provide social housing for applicants on waiting lists. The Business Model Canvas (BMC) is used to analyse the business models for affordable zero energy homes in these countries. Data is collected mainly through interviews and focus group meetings with experts. The results show that all schemes create environmental, social and economic sustainability values for low/low-middle income households by providing energy efficient, comfortable and affordable homes. Several barriers to the upscaling of these homes were identified, such as cultural barriers in design, building materials, as well as legal and technical barriers. The technical barriers can be addressed in a relatively short time, but overcoming cultural and behavioural barriers might be more difficult. Engaging government, market participants, and providers can accelerate the development of these schemes. Examples of different schemes and the courses developed during the project can be used to disseminate the results of the business models of these schemes. Finally, the business models of the schemes can be modified and adopted for the development of affordable zero energy homes in other countries.

The building sector can contribute considerably to reducing global greenhouse gas (GHG) emissions. In the Netherlands, the GHG emissions must be mitigated by 95% by 2050 relative to the 1990 baseline. Various factors, such as low renovation rates cause uncertainties in reaching these targets. The current study aims for investigating the barriers and opportunities regarding the energy efficiency renovations (EER) and programs offered by the cities in the Netherlands. Homeowners encounter individually and collectively different forms of barriers during their journeys of EERs. By collective decision-making, we mean when an individual homeowner cannot make the final decision on EER by themselves. Homeowners may have to decide together with other homeowners or even tenants in case of living in a multi-family dwelling. The local authorities sometimes offer the energy efficiency programs at neighbourhood levels and the agreement of most households is essential for the continuation of the energy efficiency programs. The literature review, semi-structured interviews and focus groups are conducted with experts from the largest cities in the Netherlands. The focus groups and interviews are used to examine the barriers and opportunities especially at neighbourhood and street levels. Our main initial findings include the barriers of (a) Individual homeowners: difficulties in making them interested to conduct EER, lack of knowledge for the starting point of renovation, additional barriers of following many steps in conducting EERs for the old dwellings; (b) Homeowner associations: difficulties in reaching agreement by 70% of homeowners, timeconsuming process for agreement, and not well-organised meetings by all the homeowners' associations; (c) Neighbourhood level: cultural diversities, difficulties in finding solutions for different groups of people, etc.

Over half of all residential buildings in the Netherlands are owner-occupied. In this study, the influence of behavioural factors on individual decisions toward energy efficiency renovations (EERs) was investigated. This study focused on contextual (e.g. building characteristics), personal (e.g. awareness of energy consumption), and motivational factors (e.g. improving comfort). Logistic regression analyses were selected as the preferred method of analysis. The Netherlands’s housing survey energy modules, which was conducted in 2018, was the basis of these analyses. 2878 homeowners were surveyed. Behavioural factors that influence the homeowners’ decisions were investigated for four types of EERs: (1) double glazing, (2) insulation, (3) photovoltaic (PV) panel, and (4) sustainable heating. It was found that homeowners’ preferences for double glazing were mainly influenced by the characteristics of the building and household and motivation to adopt EERs. Similarly, insulation and PV panels were to be mainly influenced by building characteristics. For sustainable heating, a combination of building and household characteristics and personal factors (e.g. deliberate gas reduction) influenced the decisions regarding this EER. None of the personal factors had a significant impact on the decisions regarding installation of double glazing; in contrast, the installation of PV panels was found to be highly influenced by these factors.

The renovation of housing stock in the Netherlands has the potential to help achieving the country's climate change targets. However, there are non-monetary Transaction Cost (TC) factors, such as searching for information and finding a reliable professional/contractor, that present barriers to householders when making the decision to renovate or not. This study evaluates the impact of the transaction costs on the renovation decision-making process for two groups of householders, current renovators and potential renovators, and for three types of renovations, exterior renovations, interior renovations, and energy efficiency renovations. The study analyses householder renovation decisions in relation to TC barriers at different stages of the renovation processes. The data was collected from a survey of 3,776 homeowners in the Netherlands. The main identified TC barriers were found to be at the consideration, decision, and execution phases of the renovation decision-making process, and are: finding a reliable professional/contractor to do exterior renovations, determining costs for interior renovations, and finding ways to increase the energy efficiency of the house using energy-saving renovations. The main sources of information for householders are construction stores/Do It Yourself (DIY), installations and maintenance companies for exterior and energy efficiency renovations, while for interior renovations it is construction stores/DIY companies, Internet, and recommendations from family/friends. The findings from this study contribute to more effective management and distribution of both information and financial resources in relation to the renovation of housing stock.

The European Commission aims to decrease GHG emissions to 80% below 1990 levels by 2050 (EU, 2017). The housing stock has a considerable share that equals to 40% of energy consumption and 36% of emissions in the EU. The current research aims to evaluate the homeowners' energy renovation decisions on the exterior, interior, and insulation/installation of their house. The householders' renovation decisions are analyzed with regards to (1) which stages to help/support and (2) what information is essential in the renovation process. Considering the extent of difficulty for private homeowners' in accessing information and complexity in conducting the renovation, transaction cost (TC) theory is applied in understanding the decisions. The data has been collected through a survey among 3,776 of the Dutch homeowners in 2012. Then, statistical and logistic regression analysis has been conducted to analyze the renovation decisions for two groups of homeowners: renovators and potential renovators. According to the results and the outcomes of this study: (1) For the renovators: (a) the main identified stages in getting help are in carrying out the renovation, determining the costs, and looking for the reliable professional/ contractor, (b) the main identified sources of information are at the maintenance/ installation companies, family/ friends, and via internet; (2) For the potential renovators: (a) the main identified help stages are in determining the costs, looking for a reliable professional and carrying out the renovation/ improvement, (b) the main identified sources of information are via the internet, by a maintenance/ installation companies, and family/ friends. The main difference among the renovators is for insulation/ installation. The most significant stage in getting help for insulation/installation is to find out the most efficient solutions. Similar results have been found for the potential renovators; the only difference was in the order of the influencing factors.

The housing stock has a major share in energy consumption and CO2 emissions in the Netherlands. CO2 emissions increased 2.5% year-on-year in the first quarter of 2018. Higher CO2 emissions were principally due to raised gas consumption for heating in the residential and service sector¹. Energy efficiency renovations can contribute considerably in reducing energy consumption and achieving the EU and national energy efficiency targets. However, based on recent research², the renovation rates in the Dutch social housing sector are not adequate to achieve the energy efficiency targets. Moreover, the deep renovation rates are almost negligible in this sector. The Dutch housing stock consists of the owner-occupied sector and rental sector (social housing and private rental houses) with shares equal to 69.4% and 30.6%, respectively. Considering the major share of the housing sector in energy consumption, the aim of the current study is to evaluate and compare the renovation rates in these sectors and the potential contribution of each one in achieving the energy efficiency targets. By renovation rate, we mean the percentage changes in the number of the identical houses moving from one energy label to the more efficient energy labels. The Netherlands Enterprise Agency (RVO) and Statistics Netherlands (CBS) databases are used to conduct the statistical analysis. The results show that the renovation rates are almost the same in these three sectors, despite the expectation of much higher renovation rates in the social housing sector.

The housing stock has a considerable share of 40% in energy consumption and 36% of CO ₂ emissions in the EU. In accordance to energy efficiency and emissions targets set by EU, The Netherlands has aimed to renovate 300,000 homes each year, leading to 50% reduction in CO ₂ emissions, by 2050. Many factors including low renovation rates create uncertainties in achieving these targets. The current study aims for understanding the barriers and drivers towards energy efficiency renovations (EERs) amongst Dutch homeowners, and to aid in gaining a better insight into the role of public authorities in promoting EERs. First, the extrinsic drivers, including policies and other initiatives in the EER process are explained. Second, the intrinsic drivers and intrinsic/extrinsic barriers are explored. Regression analyses are performed on the national Dutch survey data for renovators and potential renovators. Our main findings include: (a) desire to enhance the quality of their life, rather than the financial benefits, etc. is identified as the main driver; (b) the main barriers are the costs of EERs, complexities in the process, information barriers, and finding reliable experts and information; (c) For improvement in meeting renovation targets, the current Dutch policies need to consider all the decision criteria by homeowners, such as: Reducing the complexities; Time needed to obtain loans and subsidies; and Facilitating access to information.

Mobility as a Service (MaaS) is a recent innovative transport concept, anticipated to induce significant changes in the current transport practices. However, there is ambiguity surrounding the concept; it is uncertain what are the core characteristics of MaaS and in which way they can be addressed. Further, there is a lack of an assessment framework to classify their unique characteristics in a systematic manner, even though several MaaS schemes have been implemented around the world. In this study, we define this set of attributes through a literature review, which is then used to describe selected MaaS schemes and existing applications. We also examine the potential implications of the identified core characteristics of the service on the following three areas of transport practices: travel demand modelling, a supply-side analysis, and designing business model. Finally, we propose the necessary enhancements needed to deliver such an innovative service like MaaS, by establishing the state of art in those fields.