Bulletin of Surveying and Mapping ›› 2022, Vol. 0 ›› Issue (11): 149-156,161.doi: 10.13474/j.cnki.11-2246.2022.0343

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Spatio-temporal patterns of carbon emissions in Hubei province based on county level

ZHOU Xuande1, DOU Wenzhang2, ZHAN Qingming3, Zibibula·Simayi4, DENG Zutao1, ZENG Yong5   

  1. 1. School of Tourism and Hospitality Management, Hubei University of Economics, Wuhan 430205, China;
    2. School of Software & Microelectronics, Peking University, Beijing 102600, China;
    3. School of Urban Design, Wuhan University, Wuhan 430072, China;
    4. College of Recourse and Environmental Science of Xinjiang University, Urumqi 830046, China;
    5. Guizhou Duyun Economic Development Zone, Duyun 550402, China
  • Received:2021-11-03 Revised:2022-06-30 Published:2022-12-08

Abstract: Identifying the regional heterogeneity of carbon emissions is crucial for formulating effective carbon emission reduction policies. This paper uses the linear regression model, the coefficient of variation method, the Hurst index and the spatial autocorrelation analysis method to study the evolution characteristics of the spatial pattern of carbon emissions in Hubei Province. The results show that from 1997 to 2017, the carbon emission in Hubei province showed a significant upward trend of fluctuation, with an average annual growth rate of 4.74%; there were significant regional differences in the variability of carbon emission changes in the counties, and the overall presentation is “low-to-medium fluctuations, mostly high fluctuations”. The long-term correlation characteristics of carbon emission changes in the counties are obvious, and the overall characteristics are mainly moderate and strong persistence characteristics, accounting for 70%, and the distribution is relatively wide, among which the strong persistence areas are mainly concentrated in Wuhan; The spatial distribution of carbon emissions in the county area has a significant aggregation effect, showing a circle pattern with Wuhan as the core radiating continuously to the surrounding area.

Key words: carbon emissions, spatio-temporal patterns, trend analysis, Hurst index, Hubei province

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