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Vector maps and spatial autocorrelation of carbon emissions at land patch level based on multi-source data.

Xiaoping Zhang1, Qinghua Liao2, Hu Zhao1

  • 1School of Architecture and Urban Planning, Shandong Jianzhu University, Jinan, China.

Frontiers in Public Health
|November 7, 2022
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Summary

This study introduces a vectorization method for detailed carbon emissions mapping at the land patch level. This approach aids urban planning by identifying key areas and factors for carbon reduction strategies.

Keywords:
Zhangdiancarbon emissionsland patchmulti-source dataspatial autocorrelationvector maps

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Area of Science:

  • Environmental Science
  • Urban Planning
  • Geographic Information Systems (GIS)

Background:

  • Accurate carbon emissions mapping is crucial for urban planning, heat island effect mitigation, and public health.
  • Existing research lacks land patch-level carbon emissions data, hindering small-scale urban planning.
  • Urban planning requires granular data for effective carbon emission reduction strategies.

Purpose of the Study:

  • To propose a vectorization method for spatial allocation of carbon emissions at the land patch level.
  • To analyze carbon emissions and spatial autocorrelation in Zhangdian City using multi-source data.
  • To identify key land patches and influencing factors for targeted carbon reduction.

Main Methods:

  • Developed a vectorization method for land patch-level carbon emissions spatial allocation.
  • Utilized multi-source data to create vector maps of carbon emissions and spatial autocorrelation in Zhangdian City.
  • Analyzed differences between streets and compared land patch carbon emission ratios.

Main Results:

  • In 2021, Zhangdian City's total carbon emissions were 4.76 × 10^9 kg, with industrial land contributing 70.16%.
  • Forest land accounted for 98.56% of carbon absorption, primarily in peripheral areas.
  • A significant positive spatial correlation (Moran's I = 0.138) was found for land patch-level emissions; industrial structure adjustment is key.

Conclusions:

  • The vector carbon emissions map effectively identifies critical land patches and emission factors.
  • Land patch proportion and industrial structure are vital for carbon emission control.
  • Findings support targeted carbon reduction strategies, heat island mitigation, and low-carbon urban planning.