Exploring socio-economic externalities of development scenarios. An analysis of EU regions from 2008 to 2016.

A great debate around development scenarios has come to define conversations around the economy and the environment, two dimensions that struggle to find a proper balance. In this paper we apply unconditional growth model analyses to a new and unique dataset of European regions between 2008 and 2016 and identify four development scenarios - green growth, green de-growth, black growth and black de-growth - characterized by different relationships between CO2 emissions and economic growth. We then map European regions across these four scenarios and describe the differences that occurred among regions in terms of socio-economic externalities, mainly competences, investments and well-being. Drawing on our analyses, we contribute to the debate on development scenarios and ecological macro-economics, as well as discuss implications for sustainability policy and research.

Instagram Use and Mental Well-Being: The Mediating Role of Social Comparison.

ABSTRACT: Instagram has grown in popularity among young adults and adolescents and is currently the second-favorite social network in the world. Research on its relationship to mental well-being is still relatively small and has yielded contradictory results. This study explores the relationship between time spent on Instagram and depressive symptoms, self-esteem, and disordered eating attitudes in a nonclinical sample of female Instagram users aged 18-35 years. In addition, it explores the mediating role of social comparison. A total of 1172 subjects completed a one-time-only online survey. Three different mediation analyses were performed to test the hypotheses that social comparison on Instagram mediates the association time spent on Instagram with depressive symptoms (model 1), self-esteem (model 2), and disordered eating attitudes (model 3). All three models showed that the relationship between intensity of Instagram use and the respective mental health indicator is completely mediated by the tendency for social comparison on Instagram.

Multilevel-growth modelling for the study of sustainability transitions.

Sustainability Transitions (ST) is a complex phenomenon, encompassing environmental, societal and economic aspects. Its study requires a proper investigation, with the identification of a robust indicator and the definition of a suitable method of analysis. To identify the most informative geographical boundaries for analysing ST pathways, we consider the Carbon Emission Intensity (CEI) and estimate a four-level growth model to study its pattern over time for all the EU regions. We apply this model to a novel longitudinal dataset that covers CEI data of European regions at four different geographical scales (state, areas, regions, and provinces) over a nine-year timespan. This approach aims at supporting the decision-makers in developing more effective sustainability transitions policies across Europe, especially focusing on regions and overcoming the well-known "one-size fits all" approach.•The unconditional growth model has been applied to a multi-level structure considering four levels, defined by three geographical scales and time.•The ideal structure of the model would have required five levels, but the sample size of the dataset made the application computationally unfeasible;•The application of the model allowed to identify patterns of stability and change over time of the variable amongst different geographical units.

Industrial carbon emission intensity: A comprehensive dataset of European regions.

The dataset has been developed within the framework of the EU EIT-Climate Kic Flagship Project "Re-Industrialise" and it includes data of Carbon Emission Intensity (CEI) from industrial sources for the European Regions. CEI is considered as a proxy for analysing the Industrial Sustainability Transition pathways and is calculated as the ratio between CO2 equivalent emissions (CO2e) and Gross Domestic Product (GDP) of the industrial sector over a nine-year timespan, i.e. from 2008 to 2016. CO2e data at plant level have been retrieved from EU Emission Trading System (EU ETS) register and aggregated at different geographical scales, corresponding to the nested structure of NUTS (Nomenclature of Territorial Units for Statistics), proposed by EUROSTAT. Industrial GDP data have been selected from EUROSTAT database to match the industrial sectors covered by EU ETS.