Describing the location of an airport within a region, the vocabulary of urban studies is often dominated by ill-defined terms such as urban fringe, centre, suburb, corridors, etc. The dataset presented by this manuscript aims to provide a basis to describe and compare the location of 76 major European airports within their respective urban regions. The dataset consists of seven types of data: Betweenness centrality of major roads at 45 km radius of each airport region, population density, distribution of urbanized areas, location of agricultural lands, location of the natural area, and distribution of leisure and industrial sites. Ultimately, employing hierarchical clustering, five typologies of the European airport regions, given the regional location of airport, are identified: (1) Urban airports; (2) Urban periphery airports; (3) Agricultural-area airports; (4) Natural-area airports; (5) Remote airports.

Due to the sharp growth in the adaptation of electric vehicles (EV) in the Netherlands and the objectives of the Dutch Climate Accord is to encourage electric mobility, in the coming decades a substantial number of new EV charging facilities needs to be provided. Efficient planning of EV charging infrastructure is coupled with the notion of range anxiety, which is likely to be severely high in case of soon-to-be EV drivers. This study aims to estimate the cost of developing a new charging infrastructure under five scenarios of range anxiety in Amsterdam East. Employing a Linear Integer Programming optimization model, on the basis of geographic data on car registration, existing EV chargers, and electricity substations, it is obtained that if drivers use 90% of their battery before using a charging facility, the existing charging infrastructure needs to be expanded by only 31% to accommodate almost seven times larger number of EVs–the threshold set by the European Union (EU) legislation on the deployment of alternative fuel infrastructure. If drivers use only 30% of the batteries; however, an increase of 167% in infrastructure is inevitable (accounting for almost five million euro of cost). Second, at any point along the range anxiety spectrum, if the interval between charging session increases for 1 day, the overall cost decreases by more than 30%. These findings are discussed, and two policy approaches are proposed: (1) information technology approach; (2) demand-response approach, on the basis of EU legislation on energy efficiency and deployment of alternative fuel infrastructure.

EU cohesion policy, supporting a variety of projects that support regional economic development and contribute to betterment of quality of life, can be considered as a tool that is contributing to the development of a ‘sense of community’ among the EU citizens and shaping the perceptions of the EU. By investing in projects across the European territory, the policy has a direct impact on people’s daily lives and their environment. Whether and how exactly EU cohesion policy actually affects what people think about the EU remains unclear, particularly at the regional level where the policy has the most direct and palpable effects. The chapter addresses this knowledge gap by exploring the regional variation of EU identification across the regions of southern and eastern Europe. This focus is particularly relevant given that these countries are the main beneficiaries of EU cohesion policy. At the same time, many of these countries are experiencing tumultuous political developments tilting the governmental discourse towards sceptical or even hostile positions towards European integration, which is not necessarily in line with citizens’ views on the EU. The study uses Euro-barometer surveys from 2015 to explore how opinions about EU image and attachment to EU vary across southern and eastern Europe. The analysis identifies clusters of regions with similar patterns and then tries to relate these clusters to the possible role of cohesion policy in the emergence of these regional patterns.

Household energy consumption (HEC) is affected by a variety of determinants. In addition to the level of HEC in 2612 residential zones in the Netherlands (the so-called wijk) in 2014, this dataset provides a geographically-referenced data of 11 determinants of HEC on: (1) socioeconomic characteristics - namely income per capita, household size, population density; (2) urban morphology –namely buildings' surface to volume ratio, building age; (3) microclimate factors –namely number of summer days, number of frost days, humidity, wind speed at 10 m height; (4) land surface temperature; (5) normalized difference vegetation index (NDVI). The dataset is initially prepared for an analysis titled as “Land surface temperature and households' energy consumption: who is affected and where?” [1].

It is widely accepted that land surface temperature (LST) affects household energy consumption (HEC). There is, however, no previous study available that clarifies whether LST's impact is similar in each and every area, or if it varies from one location to another. Analysing the impact of LST on HEC of 2612 residential zones of the Netherlands in 2014, this study concludes that HEC of 50% of the zones is affected by LST, accounting for 0.8% of overall consumption on average. It is obtained that energy-intensive, high-income and large-size households are more likely to be affected by LST. The results show that the effect is likely to be significant in the zones with relatively milder air temperature, and higher levels of humidity and wind. It is obtained that the effect intensifies when the buildings are less compact and the zones are less urbanised. Ultimately, this study urges for a shift in the approach of the existing studies on the impact of LST by putting forward a proposition: the impact of LST on HEC could not be spatially generalised, and one cannot enhance the associations unless location-specific circumstances of the areas in question are taken into consideration.

By investing in the development of European territories, EU Cohesion Policy can be expected to have a positive impact on the citizens' views on the European Union. Whether and how the policy actually affects what people think about the EU remains unclear. This paper explores a range of regional determinants of EU image, from socio-economic to territorial factors and the intensity of EU Cohesion Policy funding, based on the data available for 2008–2015 period. It finds a positive relation between the size of the regional European Structural and Investment Funds' allocation and less negative EU image, while highlighting how a declining regional economic situation fuels more negative views on the EU. It also reveals that lower level of education and higher migration have a strong influence on negative EU image, albeit only in some European regions.

The policies of Third National Energy Efficiency Action Plan for the Netherlands, regarding the reduction of household energy consumption (HEC), were made based on the unwritten presumption that the stimuli of HEC are similar in each and every location of the Netherlands, and that it therefore is possible to formulate an identical set of incentives and regulations that are optimally suitable in all the locations of the country. The objective of this study is to examine the validity of this presumption by formulating two research questions: what are the national determinants of HEC, i.e. the stimuli that trigger the same response across the whole country? What are the local determinants of HEC, i.e. the stimuli which trigger different responses across the country? To identify local and national determinants of HEC, the impact of nine determinants of HEC in 2 462 neighbourhoods of the Netherlands is assessed by employing the geographical variability test. The results show that two of the determinants are national: (1) the number of frost-days, (2) wind speed. The results indicate that seven of the determinants are local: (1) income, (2) household size, (3) building age, (4) surface-to-volume ratio, (5) population density, (6) number of summer days, and (7) land surface temperature. By employing a semi-parametric geographically weighted regression analysis, the impact of the local and global determinants of HEC is estimated and mapped.

The vast majority of previous studies on household energy consumption (HEC) has presumed that the influencing factors of HEC are similar in each and every location regardless of the location specific circumstances. In other words, they assume that some generalizable facts explain the level of HEC and energy poverty across all areas of a city, country, region, and/or continent. At the national scale, the Third National Energy Efficiency Action Plan for the Netherlands, regarding the reduction of household energy consumption has introduced a variety of policy measures and incentives for reduction of HEC among them energy tax, reduction on VAT rate on labour cost of renovation of dwellings, energy saving agreement for rental sector, etc. Furthermore, the policy document emphasise that the geographic scope of all policy measures is “the Netherlands”. In this respect, Third National Energy Efficiency Action Plan for the Netherlands, introduce an identical set of measures and instrument for all areas of the Netherlands regardless of their location-specific circumstances. The objective of this thesis is to examine the validity of this presumption through five different studies four of which published as a scientific journal, and one of which is accepted for publication. To do so, the impact of a variety of the determinants of HEC of the Dutch neighbourhoods are studied and compared. The result of the studies shows that the impact of such determinants is spatially homogenous (i.e. similar across all neighbourhoods in question) or spatially heterogeneous (varies from one neighbourhood to another).

The number of electric vehicles in the Netherlands has sharply increased over the past decade. This has caused a need for the allocation of a substantial amount of new electric vehicle chargers around the country, which in turn has been acknowledged by a variety of legislative bodies. However, the approach of how these new charging infrastructures need to be spatially distributed has yet to be decided, including the distance that an electric vehicle charger could be allocated from the final destination of its driver. The hypothesis of this study is that if residents walk a longer distance to/from these charging stations, the chargers could be shared by a greater number of electric vehicle owners, and the total cost of the new charging infrastructure could be reduced. By using linear integer programming, the minimum cost of allocating new fast-charging stations in a central, densely populated area of Amsterdam, accounting for 7% of the city’s population, is calculated. The results show that if residents were to walk for five minutes (roughly 400 metres) instead of two and half minutes (roughly 200 metres), the overall cost of new electric vehicle chargers could be reduced by more than 1 million euros. The study also found that both the cost of new charging stations and their efficiency of use are vastly affected by the portion of the charging infrastructure that is saved for people visiting the area. The findings of this study are discussed in detail, including the proposal of potential further studies.

This study is an attempt to bridge an eminent knowledge gap in the empirical studies on Household Energy Consumption (HEC): the previous studies implicitly presumed that the relationships between HEC and the geographic drivers is uniform in different locations of a given study-area, and thus have tried to disclose such everywhere-true relationships. However, the possible spatially varying relationships between the two remain unexplored. By studying the performance of a conventional OLS model and a GWR model -adjusted R 2 , randomness of distribution of residual (tested by Moran's I), AIC and spatial stationary index of the geographic drivers, ANOVA test of residuals-this study demonstrates that the GWR model substantially provides a better understanding of HEC in the Randstad. In this respect, the core conclusion of this study is: the relationships between HEC and geographic drivers are spatially varying and therefore needed to be studied by means of geographically weighted models. Additionally, this study shows that considering spatially varying relationships between HEC and geographic drivers, by application of hierarchical clustering, the areas of the Randstad can be classified in four clusters: building age and income impact areas, building density impact areas, population density and built-up impact areas, household size and income impact areas.

Since the 1970s, a variety of studies has searched for the sociodemographic, housing and economic determinants of energy poverty. A central question, however, has not been answered by any of the previous studies: what are the national-level determinants, i.e. the determinants that homogeneously provoke a high level of energy poverty in all areas of a country? What are the neighbourhood-specific determinants, i.e. the characteristics that have a heterogeneous impact across the neighbourhoods of a country? This study seeks to answer these questions by analysing the level of energy poverty, the percentage of households’ disposable income spent on energy expenditure, in 2473 neighbourhoods of the Netherlands in 2014. By employing a semi-parametric geographically weighted regression analysis, the effects of two of the determinants of energy poverty are found to be spatially homogeneous: (i) percentage of low-income households and (ii) percentage of pensioners. The results indicate that the impacts of six of the determinants are spatially heterogeneous: (i) household size, (ii) percentage of unemployment, (iii) building age, (iv) percentage of privately rented dwellings, (v) number of summer days and (vi) number of frost days. Subsequently, the effects of spatially homogeneous and heterogeneous determinants are estimated and mapped; the results are discussed and some policy implications are proposed.

The previous studies on household energy consumption (HEC) are based on an implicit assumption: the impact of geographic determinants on HEC is uniform across a given region, and such impacts could be unveiled regardless of geographic location of households in question. Consequently, these studies have searched for global determinants which explain HEC of all areas. This study aim at examining validity of this assumption in Randstad region by putting forward a question regarding households’ gas and electricity consumption: are the determinants global, stationary across all the areas of the region, or local, varying from one location to another? By application of geographically weighted regression, impact of socioeconomic, housing, land cover and morphological indicators on HEC is studied. It is established that the determinants of HEC are local. This result led to second question: what are the main determinants of gas and electricity consumption in different neighborhoods of Randstad? The results show that variety of factors could be the most effective determinant of gas consumption in different neighborhoods: building age, household size and inhabitants’ age, inhabitants’ income and private housing tenure, building compactness. Whereas, in case of electricity consumption the picture is more deterministic: in most of the neighborhoods the most effective factors are inhabitants’ income and private tenure.

This paper explores the linkages between the characteristics of regions and EU
identification and proposes a regional typology of EU identification. Starting from a brief review of the literature on determinants of EU identification, the paper adds to it by asking questions about how these determinants operate on the regional level and how the features of regions, such territorial, governance and socio-economic characteristics, can affect the perceptions of the EU. It then reviews the existing regional typologies and data sets to identify those that can be used to describe and explain EU identification at the regional level. Following that, the paper presents a regional typology of EU identification and applies it to describe the patterns in the regional perceptions of the EU across COHESIFY case study countries. Finally, a framework for further exploration to explain the relations between the regional characteristics and EU identification is set out.

Paper produced as part H2020 project COHESIFY (Horizon 2020, Grant Agreement no 693427).

Available at: http://www.cohesify.eu/research-papers/

Despite being one of the most commonly used normative concepts in urban design, coherence still lacks a firm morphological definition. Without an explicit specification of its spatial attributes, coherence remains a vague and subjective notion of design implicitly referred to as one of the basic properties of good urban form. As a contribution to the link between urban design and morphology, this paper renders the normative concept objectively in terms of a set of quantifiable morphological indicators. Spatial proximity and consistency are suggested as the two key indicators for measuring the coherence of urban fabric. Based on the computational theory of coherence, originally put forward by Thagard, an analytical model is suggested to quantify the morphological coherence of actual urban fabrics. In this framework, three planned neighbourhoods in Rotterdam, the Netherlands are analysed to illustrate the changing nature of morphological coherence through different fashions of urbanism initiated in different periods of time.