High-resolution carbon emission mapping and spatial-temporal analysis based on multi-source geographic data: A case...

Area of Science:

• Environmental Science
• Urban Planning
• Geospatial Analysis

Background:

• Cities are major contributors to global emissions, necessitating localized climate change mitigation strategies.
• Understanding the spatial distribution of urban carbon emissions is crucial for effective policy development.
• Existing research has limited characterization of carbon emissions at the city scale.

Purpose of the Study:

• To develop and apply a novel spatialized mapping method for characterizing urban carbon emissions.
• To analyze the spatiotemporal distribution and heterogeneity of carbon emissions in Xi'an.
• To provide a scalable methodology for other cities using open-source data.

Main Methods:

• Utilized open-source geospatial data (POI, road networks, land use) to identify emission sources.
• Allocated emissions using the Intergovernmental Panel on Climate Change (IPCC) methodology.
• Employed Global Moran's I, Local Indicators of Spatial Association (LISA), and Standard Deviation Ellipses (SDE) for spatial analysis.

Main Results:

• Carbon emissions in Xi'an increased from 45.112 to 72.701 million tons between 2010 and 2021.
• High-resolution (30m) spatial distribution maps revealed detailed emission patterns, particularly in fringe areas.
• Strongest spatial autocorrelation of urban carbon emissions was observed at a 350m resolution.

Conclusions:

• The developed method provides detailed insights into urban carbon emission spatial distributions.
• Findings offer a reference for regional carbon emission reduction policies and spatial planning.
• The open-source data-driven approach is applicable to other cities for emissions management.