| 责任共担视角下中国省域物流业碳排放时空演变分析 |
| 摘要点击 238 全文点击 15 投稿时间：2024-05-07 修订日期：2024-07-10 |
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| 中文关键词 物流业 碳排放 责任共担 时空演变 热点分析 |
| 英文关键词 logistic industry carbon emission shared responsibility temporal and spatial evolution hot spot analysis |
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| 中文摘要 |
| 为了降低企业成本并推进我国实现2060年碳中和的目标,从责任共担的视角出发,基于多区域投入产出模型,深入分析了中国各省物流业碳排放的时空演变及其影响因素.通过使用Moran's I指数和局部空间自相关模型对2012~2017年的物流业碳排放进行相关性分析；同时,利用地理加权回归（GWR）模型进一步探讨了中国各省物流业碳排放的时空演变及其影响因素.结果表明,交通碳排放量在空间上呈现出显著的聚类特点.在2012~2017年间,中国各省物流业的碳排放量存在显著差异,呈现出两级分化的趋势.值得注意的是,经济水平较高的省份在流出贸易承接碳排放和物流业内需碳排放方面的占比较低.GWR模型的R2值介于0.625 715~0.765 095之间,而OLS模型的R2介于0.476 970~0.716 380之间,且GWR模型的AICc值均低于OLS模型的,表明GWR模型在解释不同影响因素与物流业碳排放之间的时空异质性方面,其拟合效果明显优于OLS模型.影响因素的异质性分析表明,物流业能源强度、周转货运量和物流业人均生产总值与物流业碳排放之间存在显著的空间正相关关系.因此,建议在制定减排策略时,应当充分考虑碳排放影响因素的时空异质性,为不同省域制定差异化的减排政策. |
| 英文摘要 |
| To reduce enterprise costs and achieve China's 2060 carbon neutrality goal at an early stage, this study analyzes in depth the spatial and temporal evolution of carbon emissions from the logistics industry in China's provinces and its influencing factors from the perspective of shared responsibility and on the basis of a multiregional input-output model. Using Moran's I index and local spatial autocorrelation model, we conducted a correlation analysis of logistics industry carbon emissions from 2012 to 2017. Additionally, based on the geographically weighted regression （GWR） model, we conducted an in-depth analysis of the spatiotemporal evolution and influencing factors of carbon emissions from the logistics industry across various provinces in China. The research results indicate that transportation carbon emissions exhibited significant spatial clustering characteristics. From 2012 to 2017, there were significant differences in the logistics industry carbon emissions among China's provinces, with a marked polarization. Provinces with higher economic levels had a lower proportion of carbon emissions associated with outbound trade and internal logistic industry demand. The R2 of the GWR model ranged from 0.625 715 to 0.765 095, whereas the R2 of the OLS model ranged from 0.476 970 to 0.716 380. Additionally, the AICc values of the GWR model were consistently lower than those of the OLS model, indicating that the GWR model provided a significantly better fit and could better explain the spatiotemporal heterogeneity between various influencing factors and logistics industry carbon emissions. The heterogeneity results of the influencing factors showed that logistic energy intensity, freight turnover, and logistic industry per capita GDP were significantly positively correlated with logistic industry carbon emissions. Therefore, the spatiotemporal heterogeneity of influencing factors on carbon emissions should be completely considered and differentiated emission reduction policies for different provinces should be formulated. |
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