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Modelling monthly-gridded carbon emissions based on nighttime light data.

Ruxing Wan1, Shuangyue Qian2, Jianhui Ruan2

  • 1School of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, China.

Journal of Environmental Management
|February 16, 2024
PubMed
Summary

This study developed a high-resolution monthly carbon emissions (CE) inventory for Guangdong using nighttime light data. Results show decreasing CE inequality and an inverted U-shaped relationship between CE and GDP, offering insights for emission reduction policies.

Keywords:
Back propagation neural networksCarbon emissionsImpact factorMonthly-griddedNighttime lightSpatial autocorrelation

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Area of Science:

  • Environmental Science
  • Geospatial Analysis
  • Climate Change Research

Background:

  • Existing carbon emissions (CE) inventories are often yearly and lack the spatiotemporal resolution needed for effective policymaking.
  • Accurate, high-resolution CE data is crucial for understanding regional variations and monthly trends to inform emission reduction strategies.
  • Guangdong province, a major economic and population center, faces significant challenges in managing its carbon emissions.

Purpose of the Study:

  • To develop a high-resolution (1 km x 1 km) monthly carbon emissions inventory using nighttime light data.
  • To evaluate the spatiotemporal variations and impact factors of CE in Guangdong province from 2013 to 2022.
  • To provide a methodological framework for high spatiotemporal resolution CE evaluation applicable to other regions.

Main Methods:

  • Utilized nighttime light (NTL) data, statistical data, and land use data to construct the CE inventory.
  • Employed a backpropagation neural network for developing the high-resolution monthly CE inventory.
  • Applied spatial autocorrelation and spatial econometric models to analyze spatiotemporal variations and impact factors of CE.

Main Results:

  • Carbon emissions in Guangdong showed unsteady increases from 2013 to 2022, with high emissions concentrated in the Pearl River Delta region.
  • The inequality of CE significantly decreased at both city and county levels, indicated by a declining Global Moran's I.
  • An inverted U-shaped relationship between CE and gross domestic product (GDP) was observed, alongside a significant positive impact of industrial structure on CE.

Conclusions:

  • The developed high-resolution monthly CE inventory provides a novel perspective for evaluating emissions spatiotemporally.
  • The findings support the existence of an Environmental Kuznets Curve for CE in Guangdong.
  • The methodology and inventory can aid in formulating targeted, regional-monthly-specific emission reduction policies.