| Autor: |
Matteo Mura, Mariolina Longo, Laura Toschi, Sara Zanni, Franco Visani, Silvia Bianconcini |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
MethodsX, Vol 8, Iss , Pp 101359- (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2215-0161 |
| DOI: |
10.1016/j.mex.2021.101359 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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