Regional representations in the EU:between diplomacy and interest mediation

What are regional representations in the European Union? What do they hope to achieve? Since the mid-1980s, sub-state actors in the EU such as county councils, Länder, Autonomous Communities, local, municipal and city authorities have been opening representative offices in Brussels – mini 'embassies' for their territories. Although on the surface these representations might look the same, in practice they operate according to very different dynamics. Whilst some rival national governments for a stake in EU policy development, others have more modest ambitions. This book offers a comprehensive assessment of the burgeoning phenomenon of regional representation in the EU. Considering evidence from old member states as well as those which joined the EU more recently, it looks at where strategies and aims differ, positioning various 'types' of representation closer to the work of embassies or to that carried out by lobbying groups. The author also considers how regional representations contribute to our understanding of multi-level governance in the EU.

The development of a regional energy assessment model

Shropshire Energy Team initiated this study to examine consumption and associated emissions in the predominantly rural county of Shropshire. Current use of energy is not sustainable in the long term and there are various approaches to dealing with the environmental problems it creates. Energy planning by a local authority for a sustainable future requires detailed energy consumption and environmental information. This information would enable target setting and the implementation of policies designed to encourage energy efficiency improvements and exploitation of renewable energy resources. This could aid regeneration strategies by providing new employment opportunities. Associated reductions in carbon dioxide and other emissions would help to meet national and international environmental targets. In the absence of this detailed information, the objective was to develop a methodology to assess energy consumption and emissions on a regional basis from 1990 onwards for all local planning authorities. This would enable a more accurate assessment of the relevant issues, such that plans are more appropriate and longer lasting. A first comprehensive set of data has been gathered from a wide range of sources and a strong correlation was found between population and energy consumption for a variety of regions across the UK. In this case the methodology was applied to the county of Shropshire to give, for the first time, estimates of primary fuel consumption, electricity consumption and associated emissions in Shropshire for 1990 to 2025. The estimates provide a suitable baseline for assessing the potential contribution renewable energy could play in meeting electricity demand in the country and in reducing emissions. The assessment indicated that in 1990 total primary fuel consumption was 63,518,018 GJ/y increasing to 119,956,465 GJ/y by 2025. This is associated with emissions of 1,129,626 t/y of carbon in 1990 rising to 1,303,282 t/y by 2025. In 1990, 22,565,713 GJ/y of the primary fuel consumption was used for generating electricity rising to 23,478,050 GJ/y in 2025. If targets to reduce primary fuel consumption are reached, then emissions of carbon would fall to 1,042,626 by 2025, if renewable energy targets were also reached then emissions of carbon would fall to 988,638 t/y by 2025.

Understanding U.S. regional linguistic variation with Twitter data analysis

We analyze a Big Data set of geo-tagged tweets for a year (Oct. 2013–Oct. 2014) to understand the regional linguistic variation in the U.S. Prior work on regional linguistic variations usually took a long time to collect data and focused on either rural or urban areas. Geo-tagged Twitter data offers an unprecedented database with rich linguistic representation of fine spatiotemporal resolution and continuity. From the one-year Twitter corpus, we extract lexical characteristics for twitter users by summarizing the frequencies of a set of lexical alternations that each user has used. We spatially aggregate and smooth each lexical characteristic to derive county-based linguistic variables, from which orthogonal dimensions are extracted using the principal component analysis (PCA). Finally a regionalization method is used to discover hierarchical dialect regions using the PCA components. The regionalization results reveal interesting linguistic regional variations in the U.S. The discovered regions not only confirm past research findings in the literature but also provide new insights and a more detailed understanding of very recent linguistic patterns in the U.S.