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The increasing threat of high-severity wildfires in Mediterranean Wildland-Urban Interface (WUI) areas demands to develop effective fire risk assessment and management strategies. Simultaneously, the newfound accessibility of spaceborne hyperspectral data represents a significant potential for generating fire severity assessments, whereas National Forest Inventories (NFI) offer a vast dataset related to vegetation and fuel loads, which is essential for shaping the planning and strategies of forest services. This research work aims to advance the state-of-the-art in WUI fire risk mapping in the western Mediterranean Basin by combining PRISMA spaceborne hyperspectral data and Spanish NFI data. The proposed methodology had three main stages: (i) fire severity assessment at local scale (a wildfire) by using PRISMA hyperspectral data and Multi-Endmember Spectral Mixture Analysis (MESMA) leveraging field-based measurements of the Composite Burn Index (70 plots); (ii) development of a high fire severity probability map at regional scale from the extrapolation of a Random Forest predictive model calibrated from fire severity estimates, NFI data and topo-climatic variables at local scale (overall accuracy = 92 %; Kappa = 0.8); and (iii) identification and characterization of zones that concentrate WUIs with high probability of high fire severity if a fire event occurs (hot-spot WUIs) by crossing the information from the previous regional high fire severity probability map and a WUI cartography developed at regional scale. Study area was Castilla y León Autonomous Region (larger Spanish region, 94,226 km 2 ), where the second-largest extreme Spanish wildfire event (28,000 ha) occurred. We identified hot-spot WUIs so that stakeholders and decision-makers could (i) prioritize resources and interventions for effective fire management and mitigation, (ii) allocate resources for prevention, and (iii) plan evacuation measures to safeguard lives and property. This study contributes to the development of next-generation fire risk assessment methods that combine remote sensing technologies with comprehensive ground-level datasets., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

What is the real meaning of sustainable innovation ? How is sustainable innovation different from other forms of innovation? How does sustainable innovation contribute to societal development? How can we measure the sustainable innovation of a country-which indicators and which synthesis methods should we use? In this paper we try to answer these previous questions. Starting from the concept of sustainable innovation, as it has been extensively discussed in recent years in a range of disciplines from economics to engineering and sociology, the paper focuses on the basic criteria (such as indicators and statistical models) required to evaluate the sustainable innovation at the regional level, choosing Italy as a case study. From the elementary indicators of innovation, on one hand, and sustainability, on other hand, a composite indicator of sustainable innovation has been computed. The statistical model used to compute this composite indicator has been a Higher-Order Partial Least Squares Path model. The results obtained applying this model to the Italian scenario are discussed, the ranking of the different Italian regions, and the impact of the composite indicator sustainable innovation on economic results of each region are discussed., (© The Author(s), under exclusive licence to Springer Nature B.V. 2021.)

Severe fires associated to climate change and land cover changes are becoming more frequent in Mediterranean Europe. The influence of environmental drivers on fire severity, especially under different environmental conditions is still not fully understood. In this study we aim to determine the main environmental variables that control fire severity in large fires (>500 ha) occurring in fire-prone ecosystems under two different environmental conditions following a transition (Mediterranean-Oceanic)-Mediterranean climatic gradient within the Iberian Peninsula, and to provide management recommendations to mitigate fire damage. We estimated fire severity as the differenced Normalized Burn Ratio, through images obtained from Landsat 8 OLI. We also examined the relative influence of pre-fire vegetation structure (vegetation composition and configuration), pre-fire weather conditions, fire history and topography on fire severity using Random Forest machine learning algorithms. The results indicated that the severity of fires occurring along the transition (Mediterranean-Oceanic)-Mediterranean climatic gradient was primarily controlled by pre-fire vegetation composition. Nevertheless, the effect of vegetation composition was strongly dependent on interactions with fire recurrence and pre-fire vegetation structural configuration. The relationship between fire severity, weather and topographic predictors was not consistent among fires occurring in the Mediterranean-Oceanic transition and Mediterranean sites. In the Mediterranean-Oceanic transition site, fire severity was determined by weather conditions (i.e., summer cumulative rainfall), rather than being associated to topography, suggesting that the control exerted by topography may be overwhelmed by weather controls. Conversely, results showed that topography only had a major effect on fire severity in the Mediterranean site. The results of this study highlight the need to prioritise fuel treatments aiming at breaking fuel continuity and reducing fuel loads as an effective management strategy to mitigate fire damage in areas of high fire recurrence., (Copyright © 2020. Published by Elsevier Ltd.)

In recent years, the share of young people not in education, employment, or training (NEETs) has shown a remarkable increase in many European countries, such as Italy. The wide diffusion of NEETs represents an alarming social issue, as being NEET predisposes young people to long-term unemployment and social exclusion. It also has a significant negative impact on the economic growth and welfare equilibrium of countries. The aim of this paper is to analyze the determinants of the NEET condition in Italy through a step by step procedure beginning with the identification of their main characteristics and then proceeding with a focus on specific homogeneous clusters of NEETs. The decomposition of the gaps in the probabilities of being NEET between the various clusters allows verifying how personal characteristics effectively act. Furthermore, the influence of unobserved factors in the professional condition of young people has been analysed in more detail through a bivariate selection probit model on the propensity to look for a job against the condition of being inactive. The results confirm the crucial role of the education system, as well as the importance of the economic and social disparities between gender and the Italian territorial districts., Competing Interests: Claudio Quintano [ORCID ID 0000-0001-8315-8476] is Emeritus Professor of Economic Statistics. He teaches “Statistical Methods for Business Decisions” and is the author of more than 400 works including essays, articles, notes, internal papers and speeches at conferences in various fields of statistics. He focused his attention on the statistical data quality and economic research. Paolo Mazzocchi [ORCID ID 0000-0002-6632-314X] is currently an Assistant Professor of Economic Statistics. His teaching experiences relate to the following subjects: “Statistics for Public Administration”, “Statistical Methods for Assessing Business Performance” and “Statistics”. His main research relates to statistical data quality in National Accounts (mainly the Non-Observed Economy) and to the analysis of the business performances. Antonella Rocca [ORCID ID 0000-0001-8171-3149] is an Assistant Professor of Economic Statistics. She teaches “Statistics for Business” and “Statistics for Enterprises”. Her many research interests are gender gap in the labour market, quality of economic data, pre-electoral political polls and social exclusion, including NEETs, immigrants and other vulnerable segments of population.The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.