|Table of Contents|

Spatial DifferentiationCharacteristicsof Carbon Emission and Its InfluencingFactors inHunan Province, China at theCounty Scale(PDF)

《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

Issue:
2024年第02期
Page:
196-210
Research Field:
环境与可持续发展
Publishing date:

Info

Title:
Spatial DifferentiationCharacteristicsof Carbon Emission and Its InfluencingFactors inHunan Province, China at theCounty Scale
Author(s):
ZHOU Hang;  ZHAO Xian-chao*
(College of Urban and Environmental Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China)
Keywords:
carbon emission;  spatial differentiation;  influencing factor;  county scale;  nightlight data;  geodetector;  GTWR model;  Hunan
PACS:
F301.24; X24
DOI:
10.19814/j.jese.2023.12007
Abstract:
Carrying outtheresearch on the spatial heterogeneity of carbon emissions and influencing factors at the countyscale is an important link to help achieve the goal of “double carbon” for counties and districts and other multi-scale regions. Using 122 counties in Hunan province as the research object, the carbon emissions of each county in Hunan province were estimated throughnightlightdata, the exploratory spatial data analysis methods were used to depict the spatial and temporal patterns of carbon emissions in the counties, and the geodetector and geographicallyand temporallyweighted regression(GTWR)model were used to quantitatively investigate the spatial differentiation ofcarbon emissionsin the countiesand the factors influencing in terms of policy, economy, energy, society, and industry. The results show that ① from 2012 to 2020, the overall trend of carbon emissionin the countiesof Hunan province is weakening and the difference is more obvious, the spatial pattern distribution is high in the north and low in the south, and the gap between the east and west is relatively small; ② the Moran's I value decreases year by year, the spatial clustering feature is more significant, and the overall positive correlation trend is shown; ③ the financial expenditures, the size of population, the urbanization rate, energy carbon intensity and agricultural development level are the dominant factors affecting carbon emissionsin the counties; ④ based on GTWR model, there are significant spatial and temporal differences in the impact of the same indicator on carbon emissions in different counties.

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Last Update: 2024-04-10