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The transition to more environmentally sustainable production processes and managerial practices is an increasingly important topic. Many industries need to undergo radical change to meet environmental sustainability requirements; the tourism industry is no exception. In this respect, a particular aspect that needs further attention is the relationship between airport performances and investments in environmental sustainability policies. This work represents a first attempt to provide empirical evidences about this relationship. Through the application of a non-parametrical method, we first assess the efficiency of the Italian airports industry. Secondly, we investigated the relationship between airports performance and management commitment toward the ecological transition using a Tobit regression model. The results show that airports' adherence to formal multi-year ecological transition programs has a positive and consistent impact on their performance.

This paper presents the R package plot3logit which enables the covariate effects of trinomial regression models to be represented graphically by means of a ternary plot. The aim of the plot is helping the interpretation of regression coefficients in terms of the effects that a change in values of regressors has on the probability distribution of the dependent variable. Such changes may involve either a single regressor, or a group of them (composite changes), and the package permits both cases to be handled in a user-friendly way. Moreover, plot3logit can compute and draw confidence regions of the effects of covariate changes and enables multiple changes and profiles to be represented and compared jointly. Upstream and downstream compatibility makes the package able to work with other R packages or applications other than R.

Additional file 1 Supplemental information 1 Methodological details, and further results.

This paper aims at investigating empirically whether and to what extent the containment measures adopted in Italy had an impact in reducing the diffusion of the COVID-19 disease across provinces. For this purpose, we extend the multivariate time-series model for infection counts proposed in Paul and Held (Stat Med 30(10):118–1136, 2011) by augmenting the model specification with B-spline regressors in order to account for complex nonlinear spatio-temporal dynamics in the propagation of the disease. The results of the model estimated on the time series of the number of infections for the Italian provinces show that the containment measures, despite being globally effective in reducing both the spread of contagion and its self-sustaining dynamics, have had nonlinear impacts across provinces. The impact has been relatively stronger in the northern local areas, where the disease occurred earlier and with a greater incidence. This evidence may be explained by the shared popular belief that the contagion was not a close-to-home problem but rather restricted to a few distant northern areas, which, in turn, might have led individuals to adhere less strictly to containment measures and lockdown rules.

Measuring the degree of spatial spreading of a sample can be of great interest when sampling from a spatial population. The commonly used spatial balance index by Grafstr\"om et al. (2012) is particularly effective in comparing the level of spatial spreading of different samples from the same population. However, its unbounded and uninterpretable scale of measurement does not allow to assess the level of spatial spreading in absolute terms and confines its use to only raw comparisons. In this paper, we introduce a new absolute measure of the spatial spreading of a sample using a normalized version of the Moran's $I$ index. The properties and behaviour of the proposed measure are analysed through two simulation experiments, one based on artificial populations and the other on a population of real business units located in the province of Siena (Italy).

International audience; English. This paper introduces a novel application of the hybrid deep neural network (DNN)-hidden Markov model (HMM) approach for automatic speech recognition (ASR) to target groups of speakers of a specific age/gender. The group-specific training of DNN is investigated and shown to be inefficient when the amount of training data is limited. To overcome this problem, the recent approach that consists in adapting a general DNN to domain/language specific data is extended to target age/gender groups in the context of hybrid DNN-HMM systems, reducing consistently the phone error rate by 15-20% relative for the three different speaker groups. Italiano. Questo articolo propone l'applicazione del modello ibrido " rete neurale artificiale multistrato-modelli di Markov nascosti " al riconoscimento automatico del parlato per gruppi di parlanti di una specifica fascia di età o genere che in questo caso sono costituiti da: bambini, maschi adulti e femmine adulte. L'addestramente della rete neurale multistrato sì e dimostrato poco efficace quando i dati di addestra-mento erano disponibili solo in piccola quantità per uno specifico gruppo di par-lanti. Per migliorare le prestazioni, un re-cente approccio proposto per adattare una rete neurale multistrato pre-addestrata ad un nuovo domino o ad una nuova linguàlinguà e stato esteso al caso di gruppi di par-lanti di diverse età e genere. L'adozione di di una rete multistrato adattata per cias-cun gruppo di parlanti ha consentito di ottenere una riduzione dell'errore nel ri-conoscimento di fonemi del 15-20% rela-tivo per ciascuno dei tre gruppi di parlanti considerati.

This PhD thesis consists of three self-contained but related essays on the topic of empirical assessment of spatial clusters of economic activities within a micro-geographic framework. The tendency of economic activities to be concentrated in a specific territory is well recognized, starting at least from the seminal studies by Alfred Marshall (Marshall, 1920). This spatial behaviour is not fortuitous; by concentrating in some areas firms enjoy a number of advantages, which then have implications for local economic growth and regional disparities and, as a consequence, are object of study in the fields of economics, geography and policy making. It has been recognized, however, that a major obstacle to further comprehension of the agglomeration phenomena of firms is the lack of a method to properly measure their spatial concentration. The most traditional measures employed by economists, indeed, are not completely reliable. Their most relevant methodological limit lies in the use of regional aggregates, which are built by referring to arbitrary definitions of the spatial units (such as provinces, regions or municipalities) and hence introduce a statistical bias arising from the chosen notion of space. This methodological problem can be tackled by using a continuous approach to space, where data are collected at the maximum level of spatial disaggregation, i.e. each firm is identified by its geographic coordinates, say (x, y), and spatial concentration is detected by referring to the distribution of distances amongst economic activities. The main purpose of the dissertation is to contribute to the development of this kind of continuous space-based measures of spatial clustering. The scientific context and motivation are outlined in depth in the first three chapters. Then the first essay introduces the space–time K-function empirical tool, proposed in spatial statistical literature, into economic literature in order to detect the geographic concentration of industries while controlling for the temporal dynamics that characterize the localization processes of firms. The proposed methodology allows to explore the possibility that the spatial and temporal phenomena, producing the observed pattern of firms at a given moment of time, interact to provide space–time clustering. The presence of significant space–time interaction implies that an observed pattern cannot be explained only by static factors but that we should also consider the dynamic evolution of the spatial concentration phenomenon. Indeed, for example, new firm settlements may display no spatial concentration if we look separately at each moment of time and yet they may present a remarkable agglomeration if we look at the overall resulting spatial distribution after a certain time period. In general, without knowing the temporal evolution of the phenomenon under study it is not possible to identify the mechanism generating its spatial structure. As a matter of fact, different underlying space–time processes can lead to resulting spatial patterns which look the same. The methodology is illustrated with an application to the analysis of the spatial distribution of the ICT industries in Rome (Italy), in the long period 1920–2005. The problem of disentangling spatial heterogeneity and spatial dependence phenomena when detecting for spatial clusters of firms is the topic of the second essay, “Measuring industrial agglomeration with inhomogeneous K-function: the case of ICT firms in Milan (Italy)”. Spatial clusters of economic activities can be the result of two distinct broad classes of phenomena: spatial heterogeneity and spatial dependence. The former arises when exogenous factors lead firms to locate in certain specific geographical zones. For instance, firms may group together in certain areas in order to exploit favourable local conditions, such as the presence of useful infrastructures, the proximity to the communication routes or more convenient local taxation systems. The phenomenon of spatial dependence, which is often of direct scientific interest, occurs instead when the presence of an economic activity in a given area attracts other firms to locate nearby. For instance, the presence of firms with a leading role encouraging the settlement of firms producing intermediate goods in the same area or the incidence of knowledge spillovers driving industrial agglomerations. This essay suggests a parametric approach based on the inhomogeneous K-function that allows to assess the endogenous effects of interaction among economic agents, namely spatial dependence, while adjusting for the exogenous effects of the characteristics of the study area, namely spatial heterogeneity. The approach is also illustrated with a case study on the spatial distribution of the ICT manufacturing industry in Milan (Italy). The third paper is titled “Weighting Ripley’s K-function to account for the firm dimension in the analysis of spatial concentration”. In the methodological context of the continuous space-based measures of spatial clustering, firms are identified as dimensionless points distributed in a planar space. In realistic circumstances, however, firms are generally far from being dimensionless and are conversely characterized by different dimension in terms of the number of employees, the product, the capital and so on. This implies that a high level of spatial concentration can occur, for example, because many small firms cluster in space, or few large firms (in the limit just one firm) cluster in space. A proper test for the presence of spatial clusters of firms should thus consider the impact of the firm dimension on industrial agglomeration. For this respect, the third essay develops a methodology based on an extension of the K-function considering firm size as a weight attached to each of the points representing the firms’ locations.

The study explores the geographical pattern of the accommodation industry in the Italian insular region of Sicily, focusing on the determinants of the risk of market exit. We adopt a standard framework of business survival analysis where agglomeration economies play an important role. We then extend the analysis by considering the role of geography to explore whether the risk of market exit depends on nearness to desirable amenities. The geography is here measured by the distance from the coast and the altitude of the place where the firm is located. When we look at the entire population of accommodation firms that started between 2010 and 2014, we find evidence that the risk of failure increases for those which are over 2 km from the coast.