Multilevel-growth modelling for the study of sustainability transitions

Abstract

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.

Keywords: Carbon emission intensity; Multilevel-growth model; Sustainability transitions; Time dependence; Transition Pathways.

Graphical abstract

Fig. 1

Empirical growth curves of CEI (T CO2 / Million € GDP) at NUTS 2 level for Germany, Italy, Spain and France. Each line, for each country, represtens the trajectory of CEI for each region (NUTS 2). On average the patterns appear lienar, thus supporting the development of a linear model.

Fig. 2

Expected slope for each country ($\widehat{{\alpha }_{1i}}$). The red dot represent the mean value of the estimated slope, while the black bar accounts for the variability of the estimated slope within the country (length equal to twice the standard deviation of the values at the NUTS 2 level). The countries are ordered on the basis of the average estimated slope, from the lowest one to the highest one. The countries with negative values show a decreasing trend for CEI, whereas the countries with positive values show a growing trend.

Fig. 3

Expected intercept for each country ($\widehat{{\alpha }_{0i}}$). The red dots represent the mean value of the estimated intercept for each country, while the black bar represents the variability of the estimated intercept within the country (equal to twice the standard deviation of the values at the NUTS 2 level).The countries are ordered on the basis of the average estimated intercept, from the lowest one to the highest one. The countries with the lowest values show a lower estimated initial level of CEI.

Fig. 4

(From [19]) Scatterplot of the estimated slope (Fig. 2) versus the estimated intercept (Fig. 3) of Carbon Emission Intensity for each country, derived from the multilevel-growth model. The figure clearly shows a negative correlation between the two values, so that the higher the estimated initial level of CEI (intercept) the lower the gradient of the line (slope of the trajectory).