Funded by: Fondazione Eni Enrico Mattei
January 2021 / December 2021

Spatial Modeling & Urban Sustainability (SpatialModUS)

Cities are the most evident representation of the spatial unevenness of economic, social and environmental dynamics. The spatial concentration of people and productions factors is responsible for the high impact that urban areas exhibit on the environment and climate but is also the engine of societal transformation toward a more sustainable economic development. The SpatialModUS project acknowledges the spatial heterogeneity underlying the ongoing economic, environmental and energetic transition and aims at deepening the analysis of spatial aspects involved in such transition. It does so leveraging on the increasing availability of remote-sensed high-resolution data and working on downscaled statistical models to understand how demography, urbanisation, economic development and climatic change interact to shape the societal transformation. Growing on the experience of previous Fondazione Eni Enrico Mattei projects such as Re-Cities, EcoValue and CitAge, the SpatialModUS project does not limit itself to the analysis of high-resolution data but aims at developing empirical research on a variety of topics connected to the sustainable urban transformation, including the transition to a low-carbon circular economy, the environmental impact of urban development and the economic analysis of urban ecosystems.

Main Results and Outputs
  • Construction of a model-based 1x1 Km resolution spatial dataset of 2050 Residential electricity demand in Italy projected considering demographic, urbanisation and climate variation;
  • Construction of a model-based 1x1 Km resolution spatial dataset of the economic impacts of climate change on GDP in 2050 in Europe projected considering multiple Shared Socioeconomic Pathways - Representative Concentration Pathways (SSP-RCP) scenarios;
  • Development of a novel methodology to map the sectoral economic impacts on GDP of multiple climatic shocks and their changing spatial distribution through dasymetric mapping and downscaling;
  • Analysis of the relation between different environmental indicators against city-specific and spatially detailed socioeconomic characteristics;
  • Analysis of the drivers and economic performance of innovations in the circular economy paradigm and study of the adoption of innovative practices and processes such as circularity and the digital economy in the manufacturing sector.
Project team

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