| 经济高质量发展背景下中国省域物流业碳排放时空分异 |
| 摘要点击 3025 全文点击 572 投稿时间：2023-11-01 修订日期：2023-12-12 |
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| 中文关键词 物流工程 碳排放 时空地理加权回归模型（GTWR） 省域物流业 低碳物流 |
| 英文关键词 logistics engineering carbon emissions geographically and temporally weighted regression（GTWR） provincial logistics industry low-carbon logistics |
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| 中文摘要 |
| 中国经济自2010年起由高速增长模式转变为高质量发展模式. 经济高质量发展期间物流业蓬勃发展,同时产生大量的碳排放,对生态环境造成严重危害. 为探明中国物流业碳排放的时空分异特征,基于Moran's I指数和双变量空间自相关模型对2010~2021年的物流业碳排放进行相关性分析；同时,基于时空地理加权回归模型（GTWR）探明中国省域物流业碳排放影响因素的时空异质性. 结果表明,统计期内中国省域物流碳排放的空间相关性逐渐从不显著的空间关系转变为显著的空间正相关性,且表现出不同程度的空间集聚性；其次,影响因素的异质性结果显示货物周转量、 物流业人均生产总值和基础设施水平与物流业碳排放呈现空间正相关性,而能源强度与物流业碳排放呈现空间负相关性. 对比地理加权回归模型（GWR）和最小二乘回归模型（OLS）结果可知,OLS模型、 GWR模型和GTWR模型调整后的R2分别为0.541、 0.567和0.838,表明所采用的GTWR模型的拟合效果最佳,能够更好地解释不同影响因素与物流业碳排放之间的时空异质性. 研究结果可为经济高质量发展下的中国制定不同省域差异化的碳减排策略提供参考. |
| 英文摘要 |
| 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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