Federico Falasca

Sustainable development goals (SDGs) have a huge impact on global policies. Contextually, the concept of ecosystem services (ESs) naturally relies on the importance of integrating human activities into a framework in which ecosystems play a fundamental role in supporting upcoming societies. Introducing ESs in the process of SDG evaluation can be useful to facilitate their achievement through national and local planning policies. Nevertheless, this aspect is still poorly addressed. In the present study, an in-depth analysis has been conducted, to associate regulation and maintenance ecosystem services (ReMESs) with the SDGs set by the 2030 Agenda. Based on the available scientific literature, ReMESs have been linked to the SDGs and SDG targets. Specific attention has been paid to deepening linkages with the SDG targets that explicitly address the introduction of ecosystem and biodiversity values into national and local planning. Finally, SDG evaluation has been further investigated, linking the SDG targets to the statistical measures proposed by the Inter-agency and Expert Group on SDGs (UN-IAEG-SDGs). This last step focused on the Italian context, according to the indicators provided by the National Institute of Statistics. The results show that ReMESs are linked to 8 out of 17 SDGs (47%). Connections decrease when considering the SDG targets (20%) and the national statistical measures (18%). It also emerges that some targets, although being connected to ReMESs, do not have the right indicators to be quantified. Ecosystem services prove to be a valid element through which modern sustainable development goals can be accomplished. This study, which highlights several gaps to be filled, wants to offer valuable help in evaluating SDGs and their implementation through ReMESs.

One of the goals of the scientific community is to equip the discipline of spatial planning with efficient tools to handle huge amounts of data. In this sense, unsupervised machine learning techniques (UMLT) can help overcome this obstacle to further the study of spatial dynamics. New machine-learning-based technologies make it possible to simulate the development of urban spatial dynamics and how they may interact with ecosystem services provided by nature. Modeling information derived from various land cover datasets, satellite earth observation and open resources such as Volunteered Geographic Information (VGI) represent a key structural step for geospatial support for land use planning. Sustainability is certainly one of the paradigms on which planning and the study of past, present and future spatial dynamics must be based. Topics such as Urban Ecosystem Services have assumed such importance that they have become a prerogative on which to guide the administration in the difficult process of transformation, taking place not only in the urban context, but also in the peri-urban one. In this paper, we present an approach aimed at analyzing the performance of clustering methods to define a standardized system for spatial planning analysis and the study of associated dynamics. The methodology built ad hoc in this research was tested in the spatial context of the city of L’Aquila (Abruzzo, Italy) to identify the urbanized and non-urbanized area with a standardized and automatic method.