Quale pianificazione per quale cittadinanza? Governo del territorio e pianificazione spaziale di fronte alla sfida della diversità religiosa

Morpurgo, Daniela

doi:10.3280/asur2022-134006

Cited by 1 publication

(2 citation statements)

References 46 publications

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“…In this case, the related feature is a ratio between the CO 2 eq. environmental indicator and a socio-demographic or territorial indicator [35,36].…”

Section: Spatial Correlation Analysis Outputmentioning

confidence: 99%

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Correlation Methodologies between Land Use and Greenhouse Gas emissions: The Case of Pavia Province (Italy)

De Lotto,

Bellati,

Moretti

2024

Air

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The authors present an analysis of the correlation between demographic and territorial indicators and greenhouse gas (GHG) emissions, emphasizing the spatial aspect using statistical methods. Particular attention is given to the application of correlation techniques, considering the spatial correlation between the involved variables, such as demographic, territorial, and environmental indicators. The demographic data include factors such as population, demographic distribution, and population density; territorial indicators include land use, particularly settlements, and road soil occupancy. The aims of this study are as follows: (1) to identify the direct relationships between these variables and emissions; (2) to evaluate the spatial dependence between geographical entities; and (3) to contribute to generating a deeper understanding of the phenomena under examination. Using spatial autocorrelation analysis, our study aims to provide a comprehensive framework of the territorial dynamics that influence the quantity of emissions. This approach can contribute to formulating more targeted environmental policies, considering the spatial nuances that characterize the relationships between demographics, territory, and GHGs. The outcome of this research is the identification of a direct formula to obtain greenhouse gas emissions from data about land use starting from the case study of Pavia Province in Italy. In the paper, the authors highlight different methodologies to compare land use and GHG emissions to select the most feasible correlation formula. The proposed procedure has been tested and can be used to promote awareness of the spatial dimension in the analysis of complex interactions between anthropogenic factors and environmental impacts.

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“…In this case, the related feature is a ratio between the CO 2 eq. environmental indicator and a socio-demographic or territorial indicator [35,36].…”

Section: Spatial Correlation Analysis Outputmentioning

confidence: 99%

“…This measure provides insights into the presence and direction of spatial autocorrelation in the data. A positive value of Moran's I indicates positive spatial autocorrelation (cluster), whereas a negative value suggests negative spatial autocorrelation (dispersion) [35,37].…”

Section: Moran's Spatial Autocorrelation Indexmentioning

confidence: 99%