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Shale gas is currently being explored in Europe as an alternative energy source to conventional oil and gas. There is, however, increasing concern about the potential environmental impacts of shale gas extraction by hydraulic fracturing (fracking). In this study, we focussed on the potential impacts on regional water resources within the Baltic Basin in Poland, both in terms of quantity and quality. The future development of the shale play was modeled for the time period 2015–2030 using the LUISA modeling framework. We formulated two scenarios which took into account the large range in technology and resource requirements, as well as two additional scenarios based on the current legislation and the potential restrictions which could be put in place. According to these scenarios, between 0.03 and 0.86 % of the total water withdrawals for all sectors could be attributed to shale gas exploitation within the study area. A screening-level assessment of the potential impact of the chemicals commonly used in fracking was carried out and showed that due to their wide range of physicochemical properties, these chemicals may pose additional pressure on freshwater ecosystems. The legislation put in place also influenced the resulting environmental impacts of shale gas extraction. Especially important are the protection of vulnerable ground and surface water resources and the promotion of more water-efficient technologies.Electronic supplementary materialThe online version of this article (doi:10.1007/s00267-015-0454-8) contains supplementary material, which is available to authorized users.

The MOLAND (MOnitoring LANd use/cover Dynamics) and the Urban Atlas (UA) are two well-known, detailed data sets of land use/cover information focused on European cities. The MOLAND data set contains a unique time series of land use/cover changes for more than thirty urban areas covering a wide temporal window (1950 to late 1990s). The UA is a more recent project that mapped land use/cover for more than 300 cities for the year 2006. In this paper we discuss the integration of both data sets in order to produce a single geo-database covering an extended time series spanning from 1950 to 2006. The different cartographic specifications of the two input data sets, particularly in terms of spatial and thematic resolution, impeded a straightforward integration. A methodology was therefore set up to harmonize the two data sets and merge them into a consistent and comparable geo-database that can be easily queried and used for both visual and analytical purposes. The usefulness of the newly integrated geo-database was demonstrated by some exploratory analyses of the urban dynamics that occurred during the time span of the combined geo-database. We further 305 306 R. R. Barranco et al. discuss the role of time series of land use/cover data and draw recommendations and directions for future work and research.

The adoption of electric vehicles (EV) has to be complemented with the right charging infrastructure roll-out. This infrastructure is already in place in many cities throughout the main markets of China, EU and USA. Public policies are both taken at regional and/or at a city level targeting both EV adoption, but also charging infrastructure management. A growing trend is the increasing idle time over the years (time an EV is connected without charging), which directly impacts on the sizing of the infrastructure, hence its cost or availability. Such a phenomenon can be regarded as an opportunity but may very well undermine the same initiatives being taken to promote adoption; in any case it must be measured, studied, and managed. The time an EV takes to charge depends on its initial/final state of charge (SOC) and the power being supplied to it. The problem however is to estimate the time the EV remains parked after charging (idle time), as it depends on many factors which simple statistical analysis cannot tackle. In this study we apply supervised machine learning to a dataset from the Netherlands and analyze three regression algorithms, Random Forest, Gradient Boosting and XGBoost, identifying the most accurate one and main influencing parameters. The model can provide useful information for EV users, policy maker and network owners to better manage the network, targeting specific variables. The best performing model is XGBoost with an R2 score of 60.32% and mean absolute error of 1.11. The parameters influencing the model the most are: The time of day in which the charging sessions start and the total energy supplied with 22.35%, 15.57% contribution respectively. Partial dependencies of variables and model performances are presented and implications on public policies discussed.

A range of scenarios for shale gas development in Poland were modelled. The impact in terms of land take and competition for land was assessed. Of land used for industrial purposes, 7-12% was attributed to shale gas extraction. If unregulated, 24% of well pads were developed within protected areas. The legislative framework can have a major influence on overall environmental impact. a b s t r a c t Scenarios for potential shale gas development were modelled for the Baltic Basin in Northern Poland for the period 2015-2030 using the land allocation model EUCS100. The main aims were to assess the associated land use requirements, conflicts with existing land use, and the influence of legislation on the environmental impact. The factors involved in estimating the suitability for placement of shale gas well pads were analysed, as well as the potential land and water requirements to define 2 technology-based scenarios, representing the highest and lowest potential environmental impact. 2 different legislative frameworks (current and restrictive) were also assessed, to give 4 combined scenarios altogether. Land consumption and allocation patterns of well pads varied substantially according to the modelled scenario. Potential landscape fragmentation and conflicts with other land users depended mainly on development rate, well pad density, existing land-use patterns, and geology. Highly complex landscapes presented numerous barriers to drilling activities, restricting the potential development patterns. The land used for shale gas development could represent a significant percentage of overall land take within the shale play. The adoption of appropriate legislation, especially the protection of natural areas and water resources, is therefore essential to minimise the related environmental impact.

Urban sprawl is a concept commonly used to describe the physical expansion of urban areas. It is traditionally associated with lower residential density, poorer connectivity, and higher energy costs for heating and transport. From the period of 1980 to 2000, the extent of the built-up area in Europe has increased at a rate three times higher than that of population increase, and urban sprawl is now recognized as a major challenge. However, for policies to address this issue, it is essential to be able to identify and quantify sprawl. Yet, there is no internationally agreed upon definition of what constitutes sprawl, nor is there an agreed upon methodology on how to measure and define it in a quantitative manner. This paper describes an attempt at characterizing urban sprawl across urban areas at a pan European scale by presenting a new indicator, the Averaged Concentric Weighted Urban Proliferation (ACWUP) index. This index is calculated by aggregating the "sprawl profile" of urban areas, derived from an adapted version of the Weighted Urban Proliferation (WUP) index and applied to EU28-wide, 100 m resolution gridded population and land-use data. In comparison to other approaches, the proposed indicator (1) is data cheap and quick to produce, and (2) provides a unique synthetic value that characterizes the sprawl status of individual cities. We believe this indicator and its associated sprawl profile could be used as a first-pass approximation that characterizes and compares urban sprawl across cities.

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There are indications that over the past years investors have been increasingly active on urban housing markets, using housing as a vehicle for wealth and investment, rather than considering it a social good. An increasingly dominant role for investors, financial agents and financial instruments on the housing market, is also known as the financialisation of housing. Assumingly, housing financialisation has negative consequences for housing affordability, driving up prices, and decreasing the affordable housing stock especially for lower and middle classes. To get a better understanding of housing financialisation and its relationship to housing affordability, the Joint Research Centre (JRC), the European Commission's science and knowledge service, conducted an exploratory study on the topic (Van Heerden, Barranco, and Lavalle, 2020).