Energy communities in the transition to a low-carbon future: A taxonomical approach and some policy dilemmas.

In calls for transition to a society in which energy production is based on renewable sources, a fundamental role is increasingly assigned to so-called 'energy communities'. The term 'energy communities' is, however, used to denote a range of different circumstances thus risking overly simplifying the phenomenon. The intention of this article is to discuss what these communities really are or could be. The article is structured into five sections. The first section introduces the main topic. The second section clarifies the use of the term community, and propose an energy community taxonomy (we consider two pairs of options which generate a four-cell matrix: a first distinction can be made between "place-based" and "non-place-based" communities on the basis of a potential correspondence between the community and a specific area; a further difference is that between communities which take shape solely for energy purposes and those with a range of objectives including goals encompassing shared management of energy systems - in this sense, we can distinguish between "single-purpose" and "multi-purpose" communities). The third section considers certain examples which test and exemplify this taxonomy. The fourth section discusses the most significant features which have emerged and considers the main implications (e.g. policy implications). The fifth section concludes by encouraging further critical debate.

Housing price gradient and immigrant population: Data from the Italian real estate market.

The database presented here was collected by Antoniucci and Marella to analyze the correlation between the housing price gradient and the immigrant population in Italy during 2016. It may also be useful in other statistical analyses, be they on the real estate market or in another branches of social science. The data sample relates to 112 Italian provincial capitals. It provides accurate information on urban structure, and specifically on urban density. The two most significant variables are original indicators constructed from official data sources: the housing price gradient, or the ratio between average prices in the center and suburbs by city; and building density, which is the average number of housing units per residential building. The housing price gradient is calculated for the two residential sub-markets, new-build and existing units, providing an original and detailed sample of the Italian residential market. Rather than average prices, the housing price gradient helps to identify potential divergences in residential market trends. As well as house prices, two other data clusters are considered: socio-economic variables, which provide a framework of each city, in terms of demographic and economic information; and various data on urban structure, which are rarely included in the same database.