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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 CO 2 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., Competing Interests: The authors confirm that there are no conflicts of interest., (© 2021 The Authors. Published by Elsevier B.V.)

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 (CO2 e ) and Gross Domestic Product (GDP) of the industrial sector over a nine-year timespan, i.e. from 2008 to 2016. CO2 e 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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article., (© 2021 The Authors.)