Post-COVID-19 Regional Resilience and the Green Economy. Case Study

The COVID-19 pandemic induced effects, predominantly negative, common throughout the national space, and the variability of spatial characteristics generated specific regional effects: in the context of the pandemic, the regions suffered common repercussions and particular, economic and social effects. Our research will focus, in addition to defining the concept of regional resilience, on analyzing some of its key indicators and ways to improve the difficulties triggered by the pandemic, for example through the “green” initiatives of local entrepreneurs. We believe that such a scientific approach can contribute to a better understanding of how strategies aimed at increasing regional economic resilience and reducing regional inequalities in our country will be developed. Thus, the paper will present the results regarding regional economic resilience, obtained by applying specific analysis methods. The specialized literature has highlighted the fact that in order to determine regional economic resilience, different mathematical-statistical analysis or evaluation tools have been used, as stated by some authors, such as Doran and Fingleton (2016). According to some specialists, such as Martin and Sunley (2015) or Christopherson et al. (2010), however, in order to obtain clarity and applicability of the concept under discussion, it is not necessarily beneficial to have a multitude of calculation methods, but rather to identify those accepted and recognized methods. This is because the tools used in the past were not always accurate, failing to predict the capacity of regions to recover in the event of economic crises according to Briguglio (2006). It should be noted that the traditional instruments used in this regard refer to employment and gross domestic product per capita as mentioned by Martin (2012), Cellini and Torrisi (2014), Fingleton et al. (2012) or Lagravinese (2015). Therefore, our paper will test regional economic resilience from the perspective of gross value added, by development regions (NUTS 2) and by counties (NUTS 3). Also, econometric models will be built with panel data for regional time series.