Characterizing urban GHG emissions based on land-use change—A case of Airport New City

Measuring greenhouse gas (GHG) emissions across different types of land is essential for urban planning during the transition to low-carbon cities. Urban land identification, based on remote sensing images and points of interest (POIs), has the potential to narrow down the statistical unit, helping achieve spatial visualization of GHG emissions below the city level and distinguish emission characteristics of different land-use types. Considering land type as a classification standard for GHG emissions, we constructed the modified STRIPAT-PLS model by identifying land-use types, reconstructing land-use GHG emissions (LUGEs) inventory, and extracting driving indexes, which calculated driving force and LUGEs that were missed in historical years. It deepened the depth and precision of GHG emissions research and provided a reference for LUGE reduction and land management below the city level where missing historical data on GHG inventories. The results showed that: 1) the modified STRIPAT-PLS model achieved a simulation accuracy of 94.9%, demonstrating the effectiveness of our research approach in depicting GHG emissions across different land-use types. 2) Land scale, socio-economic development, and industrial development were key factors that impacted agricultural land, residential land, and airport respectively. 3) Airport was the most significant carbon contributor, while industrial witnessed the highest growth in emission intensity; 4) the spatial analysis indicated decreasing differences in GHG emissions and an overall increase in emission intensity. In addition, the research proposed emission reduction priorities and strategies for each land type to promote low-carbon goals in airport cities.