In the current call for a greater human health and well-being as a sustainable development goal, to encourage active commuting to and from school (ACS) seems to be a key factor. Research focusing on the analysis of the association between environmental factors and ACS in children and adolescents has reported limited and inconclusive evidence, so more knowledge is needed about it. The main aim of this study is to examine the association between different built environmental factors of both school neighbourhood and home-school route with ACS of children and adolescents belonging to urban areas. The ACS level was evaluated using a self-reported questionnaire. Built environment variables (i.e., density of residents, street connectivity and mixed land use) within a school catchment area and home-school route characteristics (i.e., distance and pedestrian route directness—PRD) were measured using a geographic information system (GIS) and examined together with ACS levels. Subsequently, the association between environmental factors and ACS was analysed by binary logistic regression. Several cut-off points of the route measures were explored using receiver operating characteristic (ROC) curves. In addition, the PRD was further studied regarding different thresholds. The results showed that 70.5% of the participants were active and there were significant associations between most environmental factors and ACS. Most participants walked to school when routes were short (distance variable in children: OR = 0.980; p = 0.038; and adolescents: OR = 0.866; p < 0.001) and partially direct (PRD variable in children: OR = 11.334; p < 0.001; and adolescents: OR = 3.513; p < 0.001), the latter specially for children. Mixed land uses (OR = 2.037; p < 0.001) and a high density of street intersections (OR = 1.640; p < 0.001) clearly encouraged adolescents walking and slightly discouraged children walking (OR = 0.657, p = 0.010; and OR = 0.692, p = 0.025, respectively). The assessment of ACS together with the environmental factors using GIS separately for children and adolescents can inform future friendly and sustainable communities.

The European Union (EU) has assigned municipal governments a key role in the transformations needed to achieve its climate and energy objectives. One of the main initiatives of the EU has been the “The Covenant of Mayors”, launched in 2008, with impacts beyond Europe due to integration with the “Global Covenant of Mayors for Climate and Energy”. This research focuses on local measures to adapt to climate change, verifying their differences between themselves, and aims to identify and characterize patterns in the different adaptation strategies examined. Further aims are (i) the collection of good practices, framed in the Mayors Adapt initiative, managing multidimensional data from the context and from its adaptation proposals; (ii) the classification of strategies in profiles and patterns using artificial neural networks based on the previous variables; (iii) the characterization and comparison of such profiles. The results substantiate the existence of several well-differentiated approaches, connected with their geographical context, vulnerability and politics. These results provide valuable information for its interpretation and for the planning of climate change adaptation actions, highlighting the value of the creation of networks of institutional collaboration targeted at each strategic framework.

Social vulnerability, from a socio-environmental point of view, focuses on the identification of disadvantaged or vulnerable groups and the conditions and dynamics of the environments in which they live. To understand this issue, it is important to identify the factors that explain the difficulty of facing situations with a social disadvantage. Due to its complexity and multidimensionality, it is not always easy to point out the social groups and urban areas affected. This research aimed to assess the connection between certain dimensions of social vulnerability and its urban and dwelling context as a fundamental framework in which it occurs using a decision model useful for the planning of social and urban actions. For this purpose, a holistic approximation was carried out on the census and demographic data commonly used in this type of study, proposing the construction of (i) a knowledge model based on Artificial Neural Networks (Self-Organizing Map), with which a demographic profile is identified and characterized whose indicators point to a presence of social vulnerability, and (ii) a predictive model of such a profile based on rules from dwelling variables constructed by conditional inference trees. These models, in combination with Geographic Information Systems, make a decision model feasible for the prediction of social vulnerability based on housing information.

Since the middle of the last century post-industrial cities around the world have been losing population and shrinking due to the decline of their structural growth models, showing important socioeconomic transformations. This is a negative phenomenon but one that cities can benefit from. The aim of this work is to verify what type of measures against urban decline would be most suitable if applied to a specific case study. To do this, international cases of shrinking cities where successful measures were already carried out facing decline: (i) are collected, (ii) are classified based on several influencing criteria, and (iii) are grouped under similar alternatives against the decline. Measures and criteria focused on achieving sustainability are emphasized. Alternatives are then prioritised using an Analytic Hierarchy Process designed at several hierarchical levels. The results are discussed based on the construction of sustainable future scenarios according to the optimal alternatives regarding the case study, improving the model validity. The work evidences that environmental and low-cost measures encouraging the economy and increasing the quality of life, regardless of the city size-population range where they were performed, may be the most replicable. Future research lines on the integration of the method together with other decision-support systems and techniques are provided.

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