Benchmarking is a method that can be used to measure progress and create awareness about the performance of organisations. Benchmarking the housing stock energy performance of Dutch housing associations can be used to measure and assess progress towards the decarbonisation of the housing stock. A new national climate agreement was signed in 2019, and in 2021 a new method to determine the theoretical energy performance of dwellings came into force in the Netherlands. To benchmark energy performance, a set of indicators is created that adequately represents the performance of Dutch housing associations according to the changed policies. A process involving key stakeholders is presented here to identify, assess and combine possible indicators. These were then integrated into four integrated models, which led to a final benchmark model. A model was chosen that consists of three indicators covering the energy performance of Dutch housing associations. The process and arguments that led to this final model are presented. While applicable within the Dutch context, the method and research results provide generalisable insights for the creation of energy performance benchmarks for building stocks.

Achieving energy efficiency in the built environment requires extensive efforts in the renovation and adaptation of housing stock. A promising design solution is the heat pump. While gas boiler systems are commonly used in Dutch non-profit housing stock, the share of dwellings with a heat pump grew from 1.6% in 2017 to 3.2% in 2021. However, building characteristics and the energy consumption of dwellings with a heat pump are unclear. Therefore, a dataset of 69,422 dwellings with different types of heat pumps has been examined and compared to dwellings with a traditional HR107 condensing gas boiler. This research reports average characteristics and the average actual energy consumption of dwellings with all-electric, hybrid and gas absorption heat pump systems. Dwellings with a heat pump system are on average of higher building quality, their gas consumption is lower and their electricity consumption is higher than dwellings with an HR107 condensing gas boiler. Detailed insight is provided for dwellings with different heat pump systems and for dwellings with different building characteristics. Further research to determine the energy performance of dwellings with specific heat pump configurations is recommended in light of the energy transition in the built environment.

The transition towards a Circular Economy (CE) in the built environment is vital to reduce environmental impacts, resource consumption and waste generation. The built environment can be made circular by replacing building components with more circular ones. There are many circular design options for building components and knowledge about which options perform better – from an environmental perspective – is limited. Existing guidelines focussed on single components, single circular design options, applied different assessment methods and provide conflicting guidelines. Therefore, in this article, we develop environmental design guidelines by comparing multiple circular design options for two building components: a kitchen (short service life) and renovation façade (medium service life). First, we synthesize design variants based on distinct circular pathways, such as renewable-, non-virgin material use, and modularity for reuse. Second, we compare their environmental performance to a ‘business-as-usual’ variant through Material Flow Analysis (MFA) and a multi-cycle Life Cycle Assessment (LCA) including extensive sensitivity analysis on circular parameters. Analysing the 78 LCAs and MFAs, we derive 8 lessons learned on the environmental design of circular building components. We compare our findings to existing guidelines, including those for circular building structures (long service life). Amongst other lessons, we found components with a short service life benefit more from prioritizing circular design options to slow and close future cycles, whilst components with a longer service life benefit more from reducing resources and slowing loops on site. However, applying circular design options does not always result in a better environmental performance. Tipping-points were identified based on the number of use cycles, lifespans and the assessment methods applied.

In Europe, the energy performance of dwellings is measured using theoretical building energy models based on the Energy Performance of Buildings Directive (EPBD), which estimates the energy consumption of dwellings. However, literature shows large performance gaps between the theoretically predicted energy consumption and the actual energy consumption of dwellings. The goal of this paper is to investigate the extent to which empirical models provide more accurate estimations of actual energy consumption when compared to a theoretical building energy model, in order to estimate average actual energy savings of renovations. We used the Dutch non-profit housing stock to demonstrate the results. We examined three empirical models to predict the actual energy consumption of dwellings: a linear regression model, a non-linear regression model, and a machine learning model (GBM). This paper shows that these three models alleviate the performance gap by giving a good prediction of actual energy consumption on sectoral cross-sections. However, these models still have shortcomings when predicting the effects of specific renovation interventions, for example newly introduced heat pumps. The non-linear and machine learning model (GBM) outperform the theoretical model in terms of estimating energy savings through renovation interventions.

The transition towards a Circular Economy (CE) in the built environment is vital to reduce resource consumption, emissions and waste generation. To support the development of circular building components, assessment metrics are needed. Previous work identified Life Cycle Assessment (LCA) as an important method to analyse the environmental performance in a CE context. However, questions arise about how to model and calculate circular buildings components. We develop an LCA model for circular building components in four steps. First, we elaborate on the CE principles and LCA standards to identify requirements and gaps. Second, we adapt LCA standards and propose the ‘Circular Economy Life Cycle Assessment’ (CE-LCA) model. Third, we test the model by assessing an exemplary building component: the Circular Kitchen (CIK). Finally, we evaluate the CE-LCA model with 44 experts. In the CE-LCA model, building components are considered as a composite of parts and materials with different and multiple use cycles; the system boundary is extended to include these cycles, dividing the impacts using a circular allocation approach. The case of the CIK shows that the CE-LCA model supports an ex-ante assessment of circular building components in theoretical context; it makes an important step to support the transition to a circular built environment.

Introduction. The building sector consumes 40% of resources globally, produces 40% of global waste and 33% of all emissions. The transition towards a Circular Economy (CE) in the built environment is vital to achieve Sustainable Development Goals (SDGs) such as responsible consumption and production. The built environment can gradually be made circular by replacing the current 'linear' building components with circular ones during maintenance and renovation. However, there are many possible design alternatives for circular building components; knowledge on which variants perform best - from an environmental perspective - is lacking. Methods. In this article, we develop environmental design guidelines for circular building components. First, we synthesize design variants for an exemplary circular building component: the Circular Kitchen (CIK). Second, we compare the environmental performance of these variants and a 'business-as-usual' variant by applying a Material Flow Analysis (MFA) and Life Cycle Assessment (LCA). Finally, from the results, we derive design guidelines. Results. We synthesized four design variants: (1) a kitchen made from bio-based, biodegradable materials, (2) a kitchen made from re-used materials, (3) a kitchen which optimises lifespans and materials, and (4) a modular kitchen in which components (with varying lifespans) are re-used by the manufacturer. From the LCA and MFA, we derived 7 design guidelines, which include: consider building components as a composite of sub-components, parts and materials with different and multiple use-, and life-cycles; match the materialisation of each part with the expected life cycle (merely substituting for re-used or low-impact materials does not provide the most circular design); facilitate various loops (e.g., repair, re-use, recycling) simultaneously. Conclusions. The presented design guidelines can support industry in developing circular building components and, through implementation of these components, support the creation of a circular built environment.

It is commonly accepted that occupants have a significant influence on the variation in residential heating consumption. However, the scale of that influence lacks empirical investigation. The aim of this study was to distinguish which part of the variance in actual residential heating consumption can be attributed to the occupants, and which part to the building itself. This was achieved by applying and extending a method suggested by Sonderegger in 1978, using updated and significantly improved data from two different countries: the Netherlands and Denmark. These data contain different types of heating supply systems (district heating and natural gas) and different housing forms (multi and single-family social housing, and private detached single-family houses). For the studied databases, the results indicate that approximately 50% of the variance in heating consumption between houses can be explained by differences related to occupants. The other 50% can be explained by the characteristics of the building itself and other physical parameters, which are often not taken into account in simulation models of heat transmission within buildings. Additional analyses indicate that the relative influence of occupants on heating consumption differs depending on the building characteristics of the dwelling. For example, the influence of occupants is larger when the building is more energy efficient. Based on the research results, it can be concluded that it is unrealistic to aim for a building simulation model that perfectly projects residential heating consumption for individual cases. However, creating building simulation models and occupant consumption profiles that accurately represent average residential heating consumption should be possible.

Thermal renovations are considered to be an effective measure to reduce residential energy consumption. However, they often result in lower-than-expected energy savings. In this paper, we investigate some parameters that influence the probability on lower-than-expected energy savings. We do this by comparing actual pre- and post-renovation energy consumption of 90,000 houses in the Netherlands. The results of this study confirm that the effect of the parameters differ per renovation measure. For every renovation measure, the energy performance gap post renovation plays a significant role. This implies that the use of actual energy consumption data to determine the potential energy savings could therefore help to reduce the number of renovations resulting in lower-than-expected energy savings. Also, the energy efficiency state of the building pre-renovation plays an important role. One should take into account that renovations of energy inefficient buildings more frequently result in lower-than-expected energy savings than renovations of relatively energy efficient buildings. For the type of house we found that multifamily houses more often result in lower than expected savings when building installations are improved, while single-family houses renovations more frequently result in lower energy savings than expected when the building envelope insulation is improved. These insights can contribute to the decision making process whether or not to take a certain renovation measures, they can also help to manage expectations on housing stock level and individual building level.

Energy renovations often result in lower energy savings than expected. Therefore, in this study we investigate nearly 90,000 renovated dwellings in the Netherlands with pre and post renovation data of actual and calculated energy consumption. One of the main additions of this paper, compared to previous studies on thermal renovation, is that it only takes dwellings into account with the same occupants before and after renovation, using a large longitudinal dataset. Overall this paper shows new insights towards the influence of the energy efficiency state of a building prior to energy renovation, the type of building, the number of occupants, the income level of the occupants and the occupancy time on the actual energy savings, the energy saving gap and on the probability of lower energy savings than expected. We also investigate if the influence is different per type of thermal renovation measure. Some of the findings are: it is impossible to conclude which single thermal renovation measure is the most effective because this is dependent on the energy efficiency of the building prior to the energy renovation, type of building, income level and occupancy; occupants with a high income save more energy than occupants with low income; dwellings with employed occupants benefit more from improved building installations than dwellings occupied by unemployed occupants; The prebound and rebound effects are only part of the explanations for lower than expected energy savings; Deep renovations result more often in lower than expected energy savings than single renovation measures but nevertheless they result in the highest average energy saving compared to other thermal renovation measures. The results could be used for more realistic expectations of the energy reduction achieved by thermal renovations, which is important for (amongst others) policy makers, clients and contractors who make use of energy performance contracting, home owners, landlords and (social) housing associations and as a starting point to improve the energy calculation method.

Following regulation of the European Union, objectives were formulated to reduce energy consumption of the built environment in the Netherlands. For the stock of Dutch non-profit housing associations it was agreed to improve the average energy performance to an average energy index of 1.40 in 2020. This research assesses and gives insights in the progress to this objective for over 2.0 million dwellings of over 250 Dutch non-profit housing associations in 2017 and 2018. The assessment consists of an analysis of applied renovation measures, changes of the stock like new construction and demolishing, and clarifying characteristics of housing associations. It is concluded that large urban housing associations with adequate financial positions drive the improvement of the average sectoral energy performance. The improvement happens for a large part within the existing stock, mostly with traditional improvements like improved heating installations and improved insulation. Innovative solutions like: photovoltaic solar systems, combined heat and power systems, biomass systems, heat pumps and external heating, are responsible for a relative small part of the improvement within renovations. New construction and demolishing are also responsible for a relative small part of the annual improvement, but there is potential to improve this.

The difference between actual and calculated energy is called the ‘energy-performance gap’. Possible explanations for this gap are construction mistakes, improper adjusting of equipment, excessive simplification in simulation models and occupant behaviour. Many researchers and governmental institutions think the occupant is the main cause of this gap. However, only limited evidence exists for this. Therefore, an analysis is presented of actual and theoretical energy consumption based on specific household types and building characteristics. Using a large dataset (1.4 million social housing households), the average actual and theoretical energy consumptions (gas and electricity) of different household types and characteristics (income level, type of income, number of occupants and their age) were compared for each energy label. Additionally, the 10% highest and lowest energy-consuming groups were analysed. The use of combinations of occupant characteristics instead of individual occupant characteristics provides new insights into the influence of the occupant on energy demand. For example, in contrast to previous studies, low-income households consume more gas per m² (space heating and hot water) than households with a high income for all types of housing. Furthermore, the performance gap is caused not only by the occupant but also by the assumed building characteristics.