Conclusions

Two sources of data were used: the SHAERE database and the actual heating energy consumption data from Statistics Netherlands. SHAERE is a monitoring database of the energy performance of the non-profit housing stock in the Netherlands. This monitor became operational in 2010 and contains information about the energy performance of the Dutch non-profit housing sector (circa 1.2 million dwellings). Housing associations report their stock to Aedes (the umbrella organization of housing associations) at the beginning of each calendar year accounting for the previous year (e.g., in January 2014 reporting for 2013). They report the energy status of their whole dwelling stock, every year, using the Vabi Assets software, whose basis is the Dutch energy labelling methodology (ISSO, 2009). The data comprise of thermo-physical characteristics (thermal transmittance [U-value] and thermal resistance [Rc-value] values of the envelope elements, the typology of dwellings, the year of construction, etc.), heating and ventilation installations, theoretical energy consumption, CO2 emissions, the average EI (Energy Index) and more. The variables are categorized per dwelling (microdata). A considerable part of the non-profit housing stock is included in SHAERE – the response rate is more than 50% of the population, each year. The actual energy consumption data from Statistics Netherlands are collected, annually, from energy companies since 2009 (Majcen 2016). The companies report the billing data, which are calculated on the basis of the dwellings’ meter readings annually. The datasets include values of gas and electricity use. The existence of district heating is also included without, however, values of heat used, due to the lack of individual meters. The data are collected on a dwelling level based on the address, which is encrypted. The first part of this thesis (Chapter 2), used SHAERE database to examine the current energy efficiency state of the non-profit housing sector. Descriptive statistics where used to show the distribution of the thermo-physical characteristics of the stock, heating and ventilation installations and theoretical and actual heating energy consumption. This first part established the background knowledge needed to further analyse the energy renovation rate of the non-profit housing. Further, the objective, presented in Chapter 3, was to determine the energy renovation rate in the Dutch non-profit housing sector over the years 2010 - 2014. We presented an analysis of the trends of the energy improvement rate through these years, for both the whole period and also the annual values. The data used derived from SHAERE, the official tool for monitoring progress in the field of energy saving measures for the non-profit housing sector in the Netherlands. The study consisted of longitudinal data analysis using variables from the monitoring system – namely the EI and the energy labels. After establishing the energy renovation rate of the stock, we identified the energy improvements implemented in the non-profit housing sector in the Netherlands and assess their impact on the energy performance of the dwellings. We used longitudinal data and analysed the improvements of the stock for a three years’ period, namely from ultimo 2010 to ultimo 2013, based on seven different dwelling characteristics and systems. We were able to track accurately the energy improvements applied in the non-profit housing and analyse their impact on the EI for this period. The main outcome of Chapter 4 is that there are many improvements applied, but that they are too small to attain the ambitious national goal of an average EI of 1.25 in 2020. Monitoring the energy improvements of the existing housing stock can provide valuable information, concerning the energy savings that can be achieved both in terms of actual and predicted energy consumption. The predicted energy reduction in most cases differs from the actual energy consumption (Balaras et al. 2016; Filippidou et al. 2016b; Majcen et al. 2013; Tigchelaar et al. 2011). In Chapter 5 we examined the impact of thermal renovation measures on both the predicted and actual heating energy consumption of the renovated non-profit stock in the Netherlands. The actual savings revealed the real effect of renovations on the reduction of heating energy consumption and highlight the impact of (combinations of) measures on the dwellings’ performance. Having gained valuable information and experience when tracking the energy performance changes of the stock, in Chapter 6 we dealt with the estimation and prediction of future renovation rates. The accurate prediction of renovation rates can expedite the process towards emission-neutrality of the stock and assist in the design and implementation of energy efficiency policies. Using dynamic building stock modelling and statistical analyses of empirical data (SHAERE database) we predicted the energy renovation rates of the non-profit housing stock until 2050. The following sections present the conclusions and recommendations drawn from this research. Section 7.2 replies to the research questions set in the Introduction Chapter 1 of this thesis. Section 7.3 sums up the conclusions of this research. Section 7.4 brings attention to issues of data quality and monitoring as lessons learned during the realization of the research study. Sections 7.5 and 7.6 present recommendations for further research and final remarks

Energy performance progress of the Dutch non-profit housing stock: a longitudinal assessment

Worldwide, buildings consume a large part of the total energy delivered. In the context of all the end-use sectors, buildings represent the largest sector with 39% of the total final energy consumption, followed by transport in the EU (European Union ). A considerable percentage of this energy consumption is attributed to the residential sector. The building sector plays a major role in order to meet the energy saving targets set in the EU and in the Netherlands. This is particularly true for existing buildings, because they will constitute the major part of the housing stock over several decades. The renovation activity is expected to be greater than the construction and demolition activity in the future. Policy targets and regulations are in force at the EU level to ensure the energy efficiency improvement of the building stock. The Energy Performance of Buildings Directive ([EPBD] 2002, recast 2010) is the main legislative and policy tool in EU and focuses on both new and existing buildings. At the same time, the building sector plays a prominent role in the Energy Efficiency Directive ([EED] 2012). Relatedly, in the Netherlands, the foundation of energy efficiency policy has been a number of national cross-cutting measures and EU derived policies that play a large role; like the strengthening of standards for new buildings or dwellings and energy labels for existing ones. The focus of this research is the existing dwelling stock and its energy performance progress. Throughout Europe, national approaches to building stock monitoring have evolved separately. Nevertheless, monitoring the building stocks’ energy performance is gaining attention. Information about the progress of energy performance improvements is not only needed to track the progress of policy implementation, but also better information and data are necessary to help the development of roadmaps towards a more energy efficient building stock. This research seeks to provide insight into the energy performance progress, of the existing non-profit housing stock in the Netherlands, through the application of energy renovations. The non-profit housing stock comprises 30% of the housing market in the Netherlands and a large part of the policies towards a more efficient housing stock rely on the non-profit housing sector. To that end, we determine the energy renovation rate of the stock and the impact of the applied renovations on both the predicted and actual energy consumption. The difference of predicted and actual energy savings is analysed through longitudinal statistical modelling in renovated and non-renovated dwellings. Based on the knowledge gained on the renovation rates of the non-profit housing stock we compare and evaluate future renovation rates through dynamic building stock modelling and empirical data validation. In essence, we examine the effect that the improvement of thermo-physical characteristics of dwellings has on efforts to make the existing housing stock almost emission-neutral by 2050, as advocated by the European Commission since 2011

Energy efficiency state of non-profit housing stock in the Netherlands

Improving energy efficiency of buildings is widely considered as the one of the most promising, fast and cost-effective ways to mitigate climate change and achieve the 2020 and 2050 goals set for the built environment (European Commission 2011; Aedes 2017). Energy efficiency of the building stock is hard to achieve if we only focus on the design of new dwellings. In this chapter, we will analyse the energy efficiency state of the existing Dutch non-profit housing stock using data from 2015. We examine the energy efficiency state of the stock using data from the SHAERE monitoring system. The data from SHAERE include the age, type, useful floor area, thermal resistance (Rc-value) of the envelope (roof, facades and floor), thermal transmittance (U-value) of the windows, heating and domestic hot water (DHW) systems, ventilation system, the predicted heating energy consumption and energy production systems, if present. We also use actual heating energy consumption data from Statistics Netherlands to calculate the mean actual energy consumption of the stock. The chapter aims at setting the current energy performance state of the Dutch non-profit housing stock. A complete and detailed assessment of the current efficiency state of the non-profit housing stock in the Netherlands is necessary in order to examine the energy renovation pace and energy saving measures realised. The following sections present the results and conclusions drawn from this chapter. Section 2.2 presents the development of energy efficiency policies in the built environment. The SHAERE monitoring system is presented in 2.3. Section 2.4 sums up the methods used. Section 2.5 brings attention to the results of the research study. Section 2.6 presents the conclusions of this chapter

Energy performance progress of the Dutch non-profit housing stock

Worldwide, buildings consume a large part of the total energy delivered. In the context of all the end-use sectors, buildings represent the largest sector with 39% of the total final energy consumption, followed by transport in the EU (European Union )1. A considerable percentage of this energy consumption is attributed to the residential sector. The building sector plays a major role in order to meet the energy saving targets set in the EU and in the Netherlands. This is particularly true for existing buildings, because they will constitute the major part of the housing stock over several decades. The renovation activity is expected to be greater than the construction and demolition activity in the future.   Policy targets and regulations are in force at the EU level to ensure the energy efficiency improvement of the building stock. The Energy Performance of Buildings Directive ([EPBD] 2002, recast 2010) is the main legislative and policy tool in EU and focuses on both new and existing buildings. At the same time, the building sector plays a prominent role in the Energy Efficiency Directive ([EED] 2012). Relatedly, in the Netherlands, the foundation of energy efficiency policy has been a number of national cross-cutting measures and EU derived policies that play a large role; like the strengthening of standards for new buildings or dwellings and energy labels for existing ones.   The focus of this research is the existing dwelling stock and its energy performance progress. Throughout Europe, national approaches to building stock monitoring have evolved separately. Nevertheless, monitoring the building stocks’ energy performance is gaining attention. Information about the progress of energy performance improvements is not only needed to track the progress of policy implementation, but also better information and data are necessary to help the development of roadmaps towards a more energy efficient building stock.   This research seeks to provide insight into the energy performance progress, of the existing non-profit housing stock in the Netherlands, through the application of energy renovations. The non-profit housing stock comprises 30% of the housing market in the Netherlands and a large part of the policies towards a more efficient housing stock rely on the non-profit housing sector. To that end, we determine the energy renovation rate of the stock and the impact of the applied renovations on both the predicted and actual energy consumption. The difference of predicted and actual energy savings is analysed through longitudinal statistical modelling in renovated and non-renovated dwellings. Based on the knowledge gained on the renovation rates of the non-profit housing stock we compare and evaluate future renovation rates through dynamic building stock modelling and empirical data validation. In essence, we examine the effect that the improvement of thermo-physical characteristics of dwellings has on efforts to make the existing housing stock almost emission-neutral by 2050, as advocated by the European Commission since 2011

Introduction

The rapid growth of urban areas has led to the unsustainable use of resources (Langeweg et al. 2000; Bhatta 2010; UN 2014). The impacts of urban areas are evident in regions which supply cities with food, water, energy and absorb pollution and waste (UN 2014). At the same time, the current world population, of 7.6 billion, is predicted to reach 8.6 billion in 2030 and 9.8 billion in 2050 (UN 2017). Moreover, the urban population, in 2014, accounted for 54% of the total global population. This signifies a 20% increase since 1960. In 2014, the majority of people - 54% - were living in urban areas and this percentage is estimated to rise in the future (WHO 2017). In Europe, 72.5 % of European Union (EU)-28 countries inhabitants lived in cities, towns and suburbs in 2014 (Eurostat 2016b). Nevertheless, differences between countries exist. Figure 1.1 shows the urban population growth of the Netherlands in comparison to the EU, Germany, Spain, France, Slovenia and Sweden. The Netherlands is characterised by a high level of population density and a high share of urban land use, whereas in most of the Scandinavian countries and Spain much lower levels of urban land use are present (Eurostat 2016b). In this context, the main challenge is to accommodate a greater number of people while reducing the impacts on the environment, which are the main cause for climate change (IPCC 2014). Relatedly, the improvement of the quality of life of city residents is a priority (EEA 2015). Households have a large impact on energy intensity and final energy consumption, as Figure 1.2 shows. The energy intensity3 of households, depicted on the left hand side of Figure 1.2, is increasing and at the same time 25.4% of the final energy consumption in the EU 28 was attributed to the sector in 2015 (24.8% in 2016), shown on the right hand side of Figure 1.2 (EEA 2013; Eurostat 2016a). The potential for energy consumption reduction of households is large and is set as a key priority in the policy goals and directives by the European Commission (Paulou et al. 2014; Saheb et al. 2015). One of the most prominent ways to reduce the energy consumption of residential dwellings is through energy renovations

The Role of Leadership in Transitional States: The Cases of Lebanon, Israel-Palestine

View the Executive SummaryLeadership is dynamic; it is a continuous process and ever-changing relationship between numerous different factors. The research focuses on political leadership in transitional situations and the efforts of leadership in the transformation process from weak and fragmented states or communities to peaceful and viable states. These concepts are tested vis-à-vis U.S. foreign policy toward the Middle East, which will have to be adaptive and flexible on the one hand in order to show ability and will; but will also have to be consistent on the other, so that it can show commitment and impartiality.https://press.armywarcollege.edu/monographs/1475/thumbnail.jp

AUDIT ON THE QUALITY OF HANDOVERS OF A PSYCHIATRIC LIAISON TEAM IN THE UK: A SHORT REPORT

Background: The importance of handovers has been recognised and proven in clinical practice. In liaison psychiatry, this is particularly important due to the high turnover of patients seen, the shift work pattern and the number of member staff engaging in this process. An Audit on the Quality of Handovers was carried out within a Psychiatric Liaison team of a General hospital in UK with an intention to review and improve this. Method: Handovers were evaluated against the gold standard of the SBAR tool (Situation/Background/Assessment/Recommendations) over a period of 4 weeks. Handovers were assessed by 2 members of staff (a Consultant & a Specialty doctor). Data was analysed using Microsoft Excel. Results: Results showed that the team’s handover practice is mostly "Good" but there was also an amount of "Poor" under "Situation & Background", mostly presented by mid-grade doctors & trainees. Nurses scored higher than medics on overall rating, nearly 50%.This could be attributed to the fact that handovers form an essential & integral part of Nurse’s training & culture. Also mid-grade (staff grade) doctors have had a significant amount of "excellent" scoring that other groups didn\u27t have, mostly on the Assessment & Recommendations domains, which can be attributed to the fact that importance is stressed more on the assessment & treatment module by the doctors. Conclusion: The multi-disciplinary composition of the liaison psychiatry team has a positive impact on the patient care. This audit has revealed overall good communication amongst the members of the team, nevertheless one that needs some improvement, particularly amongst the doctors. Doctors tend to focus on the remedial (assessment & treatment) module rather than the holistic approach. SBAR remains an effective & handy tool to improve the handover quality. A re-audit will be carried out in 6months time to assess the improvements observed following the implementation of this new tool

Effectiveness of energy renovations: a reassessment based on actual consumption savings

Energy renovations offer unique opportunities to increase the energy efficiency of the built environment and for the existing housing stock, they are the most important solution. Usually, energy savings are based on modelling calculations. However, recent research has shown that the predicted energy consumption differs largely from the actual consumption. In this paper, the effectiveness of energy measures is re-assessed based on actual consumption data. We use a monitoring system, which contains information about the energy performance of around 60% of the Dutch non-profit housing sector (circa 1.2 million dwellings). We connect the data from this monitoring system to actual energy consumption data from Statistics Netherlands on a dwelling level. Using longitudinal analysis methods, from 2010 to 2014, we are able to identify the energy efficiency improvements of the stock and determine the effectiveness of different measures in terms of actual energy savings. The results reveal the actual energy savings of different efficiency measures and highlight the significance of the actual energy consumption when a renovation is planned or realized

Energy efficiency measures implemented in the Dutch non-profit housing sector

The existing housing stock plays a major role in meeting the energy efficiency targets set in EU member states such as the Netherlands. The non-profit housing sector in this country dominates the housing market as it represents 31% of the total housing stock. The focus of this paper is to examine the energy efficiency measures that are currently applied in this sector and their effects on the energy performance. The information necessary for the research is drawn from a monitoring system that contains data about the physical state and the energy performance of more than 1.5 million dwellings in the sector. The method followed is based on the statistical modeling and data analysis of physical properties regarding energy efficiency, general dwellings’ characteristics and energy performance of 757,614 households. The outcomes of this research provide insight in the energy efficiency measures applied to the existing residential stock. Most of the changes regard the heating and domestic hot water (DHW) systems, and the glazing. The rest of the building envelope elements are not improved at the same frequency. The results show that the goals for this sector will be hard to achieve if the same strategy for renovation is followed