[经济高质量发展背景下中国省级物流业碳排放的时空分异]。

Q2 Environmental Science
Lan-Yi Zhang, Yi-Nuo Xu, Da-Wei Weng, Shuo Wang, Xi-Sheng Hu, Rong-Zu Qiu
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引用次数: 0

摘要

2010 年以来,中国经济由高速增长阶段转向高质量发展阶段。在此期间,物流业快速发展,产生了大量的碳排放,对生态环境造成了严重威胁。为了研究中国物流业碳排放的时空变化,我们利用 Moran's I 指数和二元空间自相关模型对 2010-2021 年中国物流业碳排放进行了相关分析。此外,我们还采用地理和时间加权回归模型(GTWR)来检验省级物流相关碳排放影响因素的空间异质性。结果表明,在研究期间,中国省级物流碳排放之间的空间关系由不显著转变为显著的正空间相关。此外,还观察到不同程度的空间集聚。关于因素异质性的研究结果显示,货运周转量、物流业人均 GDP 和基础设施水平与物流碳排放呈正空间相关性,而能源强度与物流碳排放呈负空间相关性。比较地理加权回归(GWR)和普通最小二乘法回归(OLS)的结果,可以看出,OLS、GWR 和 GTWR 模型的调整 R 平方值分别为 0.541、0.567 和 0.838。这表明,我们采用的 GTWR 模型具有更高的拟合度,能更好地解释各种影响因素与物流相关碳排放之间的时空异质性。这些研究成果可为中国经济高质量发展背景下制定省域碳减排战略提供有价值的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
[Spatiotemporal Differentiation of Carbon Emissions from Logistics Industry at Provincial Scale in China Under the Background of High-quality Economic Development].

Since 2010, the Chinese economy has transitioned from a high-speed growth model to a high-quality development model. During this period, the logistics industry has witnessed rapid growth, leading to significant carbon emissions and posing severe threats to the ecological environment. To investigate the spatiotemporal variations in carbon emissions in China's logistics industry, we conducted a correlation analysis using Moran's I index and a bivariate spatial autocorrelation model from 2010 to 2021. Additionally, we employed a geographically and temporally weighted regression model (GTWR) to examine the spatial heterogeneity of factors influencing provincial-level logistics-related carbon emissions. The results indicated that over the study period, there was a shift from insignificant spatial relationships to significant positive spatial correlations among provincial-level logistics carbon emissions in China. Furthermore, varying degrees of spatial clustering were observed. The findings regarding factor heterogeneity revealed that freight turnover volume, per capita GDP of the logistics industry, and infrastructure level exhibited positive spatial correlations with logistics-related carbon emissions, whereas energy intensity showed negative spatial correlations with such emissions. Comparing the results from the geographically weighted regression (GWR) and ordinary least squares regression (OLS), it was evident that the adjusted R-squared values for the OLS, GWR, and GTWR models were 0.541, 0.567, and 0.838, respectively. This suggests that our adopted GTWR model provided a superior fit and offered better explanations for spatiotemporal heterogeneity between various influencing factors and logistics-related carbon emissions. These research findings can serve as valuable references for formulating province-specific strategies to reduce carbon emissions within China's economy under its high-quality development context.

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来源期刊
Huanjing Kexue/Environmental Science
Huanjing Kexue/Environmental Science Environmental Science-Environmental Science (all)
CiteScore
4.40
自引率
0.00%
发文量
15329
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