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A Vector Map of Carbon Emission Based on Point-Line-Area Carbon Emission Classified Allocation Method

Author

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  • Hongjiang Liu

    (School of Architecture, Tianjin University, Tianjin 300072, China)

  • Fengying Yan

    (School of Architecture, Tianjin University, Tianjin 300072, China)

  • Hua Tian

    (State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China)

Abstract

An explicit spatial carbon emission map is of great significance for reducing carbon emissions through urban planning. Previous studies have proved that, at the city scale, the vector carbon emission maps can provide more accurate spatial carbon emission estimates than gridded maps. To draw a vector carbon emission map, the spatial allocation of greenhouse gas (GHG) inventory is crucial. However, the previous methods did not consider different carbon sources and their influencing factors. This study proposes a point-line-area (P-L-A) classified allocation method for drawing a vector carbon emission map. The method has been applied in Changxing, a representative small city in China. The results show that the carbon emission map can help identify the key carbon reduction regions. The emission map of Changxing shows that high-intensity areas are concentrated in four industrial towns (accounting for about 80%) and the central city. The results also reflect the different carbon emission intensity of detailed land-use types. By comparison with other research methods, the accuracy of this method was proved. The method establishes the relationship between the GHG inventory and the basic spatial objects to conduct a vector carbon emission map, which can better serve the government to formulate carbon reduction strategies and provide support for low-carbon planning.

Suggested Citation

  • Hongjiang Liu & Fengying Yan & Hua Tian, 2020. "A Vector Map of Carbon Emission Based on Point-Line-Area Carbon Emission Classified Allocation Method," Sustainability, MDPI, vol. 12(23), pages 1-21, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:23:p:10058-:d:454917
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