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 semiparametric 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.

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 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 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. Highlights  The geographic drivers of household energy consumption are spatially varying  Household energy consumption has to be studied by geographically weighted models  Policies regarding household energy consumption need to be location-specific

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.

In this paper we show the structure of an urban design parametric system. The system is dynamic and builds an interactive relation with the designer updating the layout and related data at each input change. The responsiveness of the system allows the designer to gain awareness on the qualitative consequences of each design move by comparing a design state with a set of urban indicators and density measures which are automatically calculated along with the geometrical updates.

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