This paper focuses on the sustainable development conditions in Italian Inner Areas. Italy's Inner Areas are rural depopulated areas characterized by their distance from the main service centers of education, health, and mobility, according to the classification proposed by the National Strategy for Inner Areas in 2012. The inner and marginal areas are recently getting the interest of the market place and agricultural economics, as well as the attention of the regional and cohesion policies of national governments and the European Union. These places provide an intriguing perspective for a broader reflection on European peripheral areas and their development trajectories. The aim of this contribution is to interpret the determinants of the relationships between rural identity and perceptive components of authenticity, in order to understand the mechanisms by which they are positively reflected in socio-economic and environmental use, influencing in turn, the level of sustainability of territorial development. We found a specific role of the rural identity in the catalyzed phenomena of interaction between citizens/community and visitors/stakeholders, through the authenticity effect, identifying them as the basis of the spontaneous, bottom-up emersion of a symbolic platform, which characterizes the identification of a place brand and of the creation of the perception/destination of the inner territories' characteristics. Finally, the paper discusses an Italian Inner Areas project promoted by the common engagement of local institutions and social and economic actors.

The paper focuses on the effects of the Covid-19 pandemic on some Italian farms. In particular, the aim is to investigate the consequences of the health emergency on diversified farms, their reactions, and their agricultural and rural policy needs in order to overcome the crisis. The research path investigates five farms of central Italy through semi-structured interviews. The identified case studies are characterized by the heterogeneity of features and farms’ activities. These activities include agritourism, on-farm processing of plant and animal products (mainly olive oils, fruits, and cheese), bio-energy production, tastings and leisure activities, educational farms, and contracting of farm equipment. A qualitative–quantitative analysis based on textual analysis techniques, particularly content and sentiment analysis, was performed. The results highlight the importance of farm diversification and networks in farms’ strategies in dealing with the Covid-19 crisis. Furthermore, the presence of both synergies and trade-offs in different types of diversification is found. These results have interesting policy implications that should be more explicitly taken into account to target the next rural development measures.

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This paper focuses on the analysis on income inequality in Italy at the municipal level of the areas defined by the National Strategy for Inner Areas. We discuss an analysis of the economic and spatial dynamics of the phenomenon through the construction of the Gini's coefficient and the estimation of the regression model for the evaluation of the determinants of inequality. We highlight the influence of the spatial dimension on income inequality in Italy. Inequality appears to be greater in densely populated urban centers with a strong incidence of tertiary activities and young population. Conversely, in the inner areas, the distribution of income is more balanced due probably to the weakness of the social and economic structure that determines low levels of income and job opportunities mainly in the agricultural sector. Sustainability 2020, 12, 1622 2 of 18 Sustainability 2020, 12, x FOR PEER REVIEW 11 of 18 355 Figure 2. Map of significance of the LM index for the values of the Gini coefficient. Source: own 356 elaboration. 357Consequently, the analyses are based on a classic linear regression model. Table 4 shows its main 358 results. The histogram of the residuals is showed Figure 3. 359A first element of reflection is that the model explains 60% of the observed variability, 360 confirming the strong territorial characterization of inequality in Italy. A second consideration is that, 361 except L, all the selected variables are quite significant. In detail, the t ratio between coefficients and 362 standard errors reports the relative variation of Gini coefficent, associated with a unitary variation of 363 the explanatory variables. The last columns on the right show the p values and the relative 364 significance under the hypothesis of robust standard errors with respect to heteroskedasticity (i.e., in 365 the presence of a nonconstant variance between the residuals or discrepancies between observed and 366 estimated values of the dependent variable, a possible cause of distorted estimations of the model 367 parameters). 369Lisa significance map High-High (928) High-Low (39) Low-High (27) Low-Low (809)