Thematic Area Label Description Source Year Population Pop.2017 Total population on 1st January Eurostat [demo_r_d2jan] 2017 % Pop. >65 People aged 65 and over (% of total population) elaboration on Eurostat [demo_r_pjanaggr3] 2017 % Pop. <15 People aged less than 15 (% of total population) elaboration on Eurostat [demo_r_pjanaggr3] 2017 % foreigners Foreign (EU and non-EU countries) population (% of total population) elaboration on Eurostat [cens_11ctzo_r2] 2011 Pop. Density Population density (per km2) Eurostat [demo_r_d3dens] 2017 % Pop upper secondary/ tertiary educ. Population aged 25-64, with upper secondary and tertiary education (ISCED levels 3-8) (% of total population) Eurostat [edat_lfse_04] 2017 Economy and labour market per capita GDP Gross domestic product (GDP) at current market prices in Purchasing Power Standard, per inhabitant Eurostat [nama_10r_2gdp] 2016 Unempl. Rate Unemployment rate (15-74 years) Eurostat [lfst_r_lfu3rt] 2017 Empl. Rate Employment rate (15-64 years) Eurostat [lfst_r_lfe2emprt] 2017 Sectoral structure* Due to the presence of missing values in the structural business statistics (SBS) section, the values for year 2015 can be replaced with the values from the closest available year (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015).However, for some regions and some NACE Rev.2 divisions, no data are available for the whole period under consideration. In these cases, a value equal to 0 is assigned to the specific region/division.

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As a consequence of climate change, the impact of pluvial flooding is expected to increase in the next decades. Despite citizens’ poor knowledge, several types of stormwater infrastructure can be implemented to mitigate the impact of future events. This paper focuses on the implementation of green and grey stormwater interventions (i.e., with or without vegetation) on private properties. Framed by the Protection Motivation Theory, a survey-based case study analysis, carried out in a pluvial flooding-prone area of the Veneto Region (Italy), highlights the main factors driving people’s willingness to implement these interventions. The analysis shows that the implementation of grey stormwater infrastructures is driven by the perceived threat and the amount of past pluvial flooding damage (i.e., the direct experience as a proxy of prior knowledge) while the implementation of green stormwater infrastructures is driven also by additional factors (awareness of these interventions, age and education level of the citizens). Based on these results, lack of knowledge on innovative stormwater interventions represents a critical barrier to their implementation on private properties, and it confirms the need for specific dissemination and information activities.

This paper aims to provide a multidimensional and continuous indicator of rurality by means of fuzzy logic. A nuanced urban-rural continuum actually occurs in an enlarged Europe. Thus, the introduction of a fuzzy rurality indicator (FRI) can improve conventional urban-rural classifications. FRI takes into account about 1,300 NUTS 3 regions throughout the EU 27. Being a continuous indicator, it highlights those nuances, in a multidimensional perspective: it covers complementary drivers, such as role of agriculture, population density and land use characteristics. The paper also stresses geographical differences among EU Countries and groups of them. In this respect, replacing Eurostat urban-rural typologies, FRI improves both accuracy of further socio-economic analyses and policy evaluations

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This paper applies multidimensional clustering of EU-28 regions with regard to their specialisation strategies and socioeconomic characteristics. It builds on an original dataset.Several academic studies discuss the relevant issues to be addressed by innovation and regional development policies, but so far no systematic analysis has linked the different aspects of EU regions research and innovation strategies (RIS3) and their socio-economic characteristics. This paper intends to fill this gap, with the aim to provide clues for more effective regional and innovation policies.In the data set analysed in this paper, the socioeconomic and demographic classification associates each region to one categorical variable (with 19 categories), while the classification of the RIS3 priorities clustering was performed separately on "descriptions" (21 Boolean categories) and "codes" (11 Boolean Categories) of regions' RIS3. The cluster analysis, implemented on the results of the correspondence analysis on the three sets of categories, returns 9 groups of regions that are similar in terms of priorities and socioeconomic characteristics. Each group has different characteristics that revolve mainly around the concepts of selectivity (group's ability to represent a category) and homogeneity (similarity in the group with respect to one category) with respect to the different classifications on which the analysis is based.Policy implications showed in this paper are discussed as a contribution to the current debate on post-2020 European Cohesion Policy, which aims at orienting public policies toward the reduction of regional disparities and to the enhance complementarities and synergies within macro-regions.

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