Land Consumption Dynamics and Urban–Rural Continuum Mapping in Italy for SDG 11.3.1 Indicator Assessment

Abstract

For the first time in human history, over half of the world’s population lives in urban areas. This rapid growth makes cities more vulnerable, increasing the need to monitor urban dynamics and its sustainability. The aim of this work is to examine the spatial extent of urban areas, to identify the urban–rural continuum, to understand urbanization processes, and to monitor Sustainable Development Goal 11. In this paper, we apply the methodology developed by the European Commission-Joint Research Center for the classification of the degree of urbanization of the Italian territory, using the ISPRA land consumption map and the ISTAT population data. The analysis shows that the availability of detailed and updated spatialized population data is essential to calculate SDG indicator 11.3.1, which assesses the ratio of land consumption rate to population growth rate. Three new indicators are also proposed to describe the main trends in urban sprawl, analyzing the spatial distribution of land consumption in terms of infill and settlement dispersion. The research shows good results in identifying class boundaries and describing the Italian urbanized landscape, highlighting the need for more detailed spatialized demographic data. The classification obtained lends itself to a variety of applications, such as monitoring land consumption, settlement dynamics, or the urban heat islands, and assessing the presence and state of green infrastructures in the urban context, driving the development of policies in urban areas toward sustainable choices focused on urban regeneration.

Land Cover Mapping with Convolutional Neural Networks Using Sentinel-2 Images: Case Study of Rome

“…This theme is included among the priorities of the 7th of the 8th Environmental Action Programme, is included in the "Biodiversity Strategy for 2030", is the theme of the UN Sustainable Development Goals number 11 and requires the adoption of effective policies based on reliable and timely observations. Actually, the methodology stands as a promising tool to deepen the monitoring of urban areas and in particular the green infrastructures in urban areas and the related ecosystem services, the evaluation of the cooling index, the SDG 11.7.1 [84] associated with accessibility, and maps of vulnerability in terms of physical access to green spaces. Experiences such as the recent "Dynamic world" [85] show the potential of deep learning techniques in land cover mapping, even on a large scale and with high update frequency.…”

Green Consumption, Environmental Regulation and Carbon Emissions—An Empirical Study Based on a PVAR Model

“…As a new consumption concept, it faces a series of new challenges and problems in practical exploration and theoretical research. Past studies have been conducted from five perspectives: green consumption theory [4][5][6][7], a green consumption model [8][9][10], green consumption marketing strategy [11][12][13], the green consumption attitude-behavior gap [14][15][16], and factors affecting green consumption [17][18][19][20][21].…”

Urban dispersion indicator to assess the Italian settlement pattern

“…This research aims to overcome this limitation by using national-scale data with a much higher spatial resolution (10 × 10 m) (ISPRA, 2022). This allows us to analyze the urban sprinkling phenomenon in detail and comprehensively understand urban expansion dynamics, especially in cases where traditional global approaches prove limited (Cimini et al, 2023). In summary, we seek to bridge a critical gap in our understanding of urbanization by utilizing more detailed and higher-resolution data.…”