文章基本信息

标题：Multilevel-growth modelling for the study of sustainability transitions
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作者：Matteo Mura ; Mariolina Longo ; Laura Toschi 等
期刊名称：MethodsX
印刷版ISSN：2215-0161
电子版ISSN：2215-0161
出版年度：2021
卷号：8
页码：1-8
DOI：10.1016/j.mex.2021.101359
语种：English
出版社：Elsevier
摘要：AbstractSustainability 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.Graphical abstractDisplay Omitted
关键词：Multilevel-growth model;Sustainability transitions;Carbon emission intensity;Time dependence;Transition Pathways