Analysis of Dynamic Evolution and Spatial-Temporal Heterogeneity of Carbon Emissions at County Level along “The Belt and Road”—A Case Study of Northwest China

Shaoqi Sun, Yuanli Xie, Yunmei Li, Kansheng Yuan () and Lifa Hu
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Shaoqi Sun: College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
Yuanli Xie: College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
Yunmei Li: College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
Kansheng Yuan: College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
Lifa Hu: College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China

IJERPH, 2022, vol. 19, issue 20, 1-20

Abstract: Northwest region is the main energy supply and consumption area in China. Scientifically estimating carbon emissions (CE) at the county level and analyzing the spatial-temporal characteristics and influencing factors of CE in a long time series are of great significance for formulating targeted CE reduction plans. In this paper, Landscan data are used to assist NPP-VIIRS-like data to simulate the CE from 2001 to 2019. Spatial-temporal heterogeneity of CE was analyzed by using a two-stage nested Theil index and geographically and temporally weighted regression model (GTWR). The CE in northwest China at the county increases yearly while the growth rate slows down from 2001 to 2019. The spatial pattern forms a circle expansion centered on the high-value areas represented by the provincial capital, which is also obvious at the border between Shaanxi and Ningxia. Axial expansion along the Hexi Corridor is conspicuous. The spatial pattern of CE conforms to the Pareto principle; the spatial correlation of CE in northwest counties is increasing year by year, and the high-high agglomeration areas are expanding continuously. It is an obvious high carbon spillover effect. Restricted by the ecological environment, the southwest of Qinghai and the Qinling-Daba Mountain area are stable low-low agglomeration areas. The spatial pattern of CE in northwest China shows remarkable spatial heterogeneity. The difference within regions is greater than that between regions. The “convergence within groups and divergence between groups” changing trend is obvious. According to the five-year socioeconomic indicators, the economic scale (GDP), population scale (POP), and urbanization level (UR) are the main influencing factors. The direction and intensity of the effect have changed in time and space. The same factor shows different action intensities in different regions.

Keywords: carbon emission; energy consumption; nighttime light data; spatial-temporal heterogeneity; two-stage nested Theil index; geographically and temporally weighted regression (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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