Walkability is a modern concept that has become important in recent years due to the doubtless effects it has on aspects such as health and wellbeing, sustainable development, climate change, and tourism. It is necessary, therefore, that urban development strategies aim to achieve walkable cities. The main objective of this study is to define a methodology to calculate the walkability index in tourist cities and to predict the effects of climate change on this index, which is applied to three World Heritage cities in central Spain: Salamanca, Ávila, and Segovia. The methodology is developed in three phases. Phase I focus on the calculation of walkability, considering the following factors: facilities and services, accessibility, sidewalk width, population density, green areas, and urban trees. In Phase II, walkability in 2020, climate-related variables were added to the previous result: temperatures, solar radiation, and shadows. Finally, the third phase, walkability under climate change pressure in 2030, 2050, and 2100, establish predictions for different climate scenarios. The results show excellent walkability indices (higher) in city centers and newly built neighborhoods and low values in the rest of the peripheral areas, industrial estates, and neighborhoods. Climate predictions showed a generalized decrease in walkability over time, even higher in the scenario with high greenhouse gas emissions. Likewise, the models can be an excellent tool for the tourist management of cities since they show the most walkable areas and, therefore, the most suitable for tourist routes.

This paper presents a multi-criteria methodology for the detection of optimal locations for solar photovoltaic installations connected to the electrical grid. The proposed methodology has been applied to the island of Tenerife, as it is one of the territories in Spain with the greatest solar potential. This methodology integrates an Aptitude Model (which covers variables such as connections to the electrical grid, accessibility, cloudiness, solar irradiation and slope) together with an Impact Model (which considers variables such as landscape vulnerability, land use and hydrology). Each one of the variables considered has been transformed into standardized decision criteria, which have been weighted by means of Saaty’s pair method, having also assigned them relative weights by means of expert consultation. The integration of both Models in a Hosting Capacity Model makes it possible to consider urban and environmental constraints in different possible scenarios. Finally, the Hosting Capacity Model generated is implemented through a Geographic Information System (GIS) on the island of Tenerife, so that it has been possible to detect the optimum locations for each municipality and region.

The internal characteristics of dwellings and their level of accessibility to basic services have traditionally been the main variables in establishing their level of quality; however, in large cities, problems associated with traffic and environmental quality are becoming increasingly important because of their impact on people’s quality of life. This paper aims to build a well-being index for dwellings in the central districts of Madrid that can help the urban managers to take decisions increasing citizens’ well-being. For this purpose, we have developed an indicator using the P2-distance method on a database of variables related to environmental quality, and on the other hand, variables related to the characteristics of the dwelling. We find that, in general, the distance to services variables correlates negatively with the well-being index as we expected. On the other side, a positive and unexpected correlation is observed between the well-being index and the variables of pollution and noise, which indicates that these variables are usually higher in the points best located with respect to basic services, places that we could consider as service centers of the central almond. Therefore, the index allows urban planners to test complex solutions in presence of the trade-offs between elements that affect negatively the well-being such as noise or pollution, versus positive variables such as higher density of services availability.

The implantation of wind farms in the European territory is being deployed at an accelerated pace. In the proposed framework, the province of La Coruña in the autonomous community of Galicia is tested, with a wide deployment of this type of infrastructure in the territory initiated in the 80s, representing the third autonomous community with the largest exploitation of wind resources, which provides sufficient information, extrapolated to the entire community, to demonstrate the practical usefulness and potential of the method of obtaining the territorial model proposed in this article The regional has been used as the basic administrative subunit of the study variables, considering that the territory thus delimited could have common physical and cultural characteristics. The methodology presented in this article involves the collection and processing of public cartographic data on various factors most repeatedly or agreed upon in the consulted bibliography based on studies by experts in the technical, environmental, and environmental areas, including explanatory variables of risk in a broader context of climate change as the first contribution of this study. Another contribution is the inclusion in the model of the synergistic impact measured as the distance to wind farms in operation (21% of the total area of the sample) to which an area of influence of 4 times the rotor diameter of each of the wind turbines im-planted has been added as a legal and physical restriction. On a solid basis of selection of explanatory variables and with the help of Geographic Information Systems (GIS) and multi-criteria analysis (MCDM), techniques widely documented in the existing literature for the determination of optimal areas for the implementation of this type of infrastructure, a methodological proposal is presented for the development of a strategic, long-term territorial model, for the prioritization of acceptable areas for the implementation of wind farms, including forecasts of increased energy demand due to the effect of climate change and the population dynamics of the study region that may influence energy consumption. This article focuses on the use of multivariate clustering techniques and spatial analysis to identify priority areas for long-term sustainable wind energy projects. With the proposed strategic territorial model, it has been possible to demonstrate that it is not only capable of discriminating between three categories of acceptable areas for the implementation of wind farms, taking into account population and climate change forecasts, but also that it also locates areas that could require conservationist measures to protect new spaces or to recover the soil because they present high levels of risk due to natural or anthropic disasters considered. The results show acceptable areas for wind energy implementation, 23% of the total area of the sample, 3% conservation as ecological spaces to be preserved, and 7% recovery due to high-risk rates. The findings show that coastal regions generally show a more positive carrying capacity, likely due to less dense development or regulatory measures protecting these areas. In contrast, certain inland regions show more negative values, suggesting these areas might be experiencing higher ecological disturbance from construction activities. This information highlights the importance of strategic site analysis to balance energy production with conservation needs. The study provides insights into wind farm deployment that considers the visual and ecological characteristics of the landscape, promoting sustainability and community acceptance. For this reason, these insights can be effectively used for advancing renewable energy infrastructures within the European Union’s energy transition goals, particularly under the climate and energy objectives set for 2030.