中国省域物流碳排放效率的演化特征及收敛性分析

doi:10.3969/j.issn.1004-9479.2024.04.20220192

摘要/Abstract

摘要：

以2006—2019年中国30个省域为研究对象，基于碳排放新评估准则，结合超效率DEA-ML模型和全局空间自相关分析，对中国实际物流碳排放效率进行重新测度与总体特征描述，进一步引入探索性时空数据分析方法及相关计量模型，深入探讨物流碳排放效率时空动态演化特征及其对收敛性的影响。结果表明：①中国物流碳排放效率处于中等水平，平均情况呈东高西低分布，大多省份处于规模报酬递增阶段，年际变动表现出动态增长趋势，受技术进步影响大，存在空间集聚特征。②物流碳排放效率的局部空间结构与空间依赖方向变化存在差异，在相邻省份间呈现较强的空间整合性，其时空迁跃具有转移惰性与路径锁定的功能。③省域物流碳排放效率不存在σ收敛，存在β收敛和俱乐部收敛。地区差异、物流集聚水平与信息化程度是条件β收敛的主要影响因素，物流碳排放效率时空演化更为曲折的地区存在更为显著的俱乐部收敛。

关键词: 物流碳排放效率, 时空演化, 收敛性分析, 探索性时空数据分析

Abstract:

30 provinces in China from 2006 to 2019 were studied, the Super-efficiency DEA-ML model and global spatial autocorrelation analysis method were combined to re-measure the actual logistics carbon emission efficiency and its overall characteristics in China, which based on the new assessment criteria for carbon emissions. The exploratory space-time data analysis method and related measurement models were further introduced to explore the space-time dynamic evolution characteristics of logistics carbon emission efficiency and its impact on convergence. The results suggest that: ①The efficiency of logistics carbon emissions is at a medium level in China and exists spatial agglomeration. The average is high in the east and low in the west. Increasing returns to scale exists in most provinces. Interannual variation shows a dynamic growth trend, and is greatly influenced by technological progress. ②The local spatial structure of logistics carbon emission efficiency differs from the direction of spatial dependence, The logistics carbon emission efficiency presents strong spatial integration among neighboring provinces, and has transfer inertia and path locking during dynamic evolution. ③Logistics carbon emission efficiency has no σ convergence and has β convergence and club convergence. Regional differences, logistics agglomeration level and degree of informatization are the main influencing factors of conditional β convergence, there is significant club convergence in areas with more tortuous temporal and spatial evolution of logistics carbon emission efficiency.

Key words: logistics carbon emission efficiency, spatiotemporal evolution, convergence analysis, exploratory space-time data analysis

李晓梅, 黄俊. 中国省域物流碳排放效率的演化特征及收敛性分析[J]. 世界地理研究, 2024, 33(4): 155-166.

Xiaomei LI, Jun HUANG. Evolution characteristics and convergence analysis of carbon emission efficiency of provincial logistics in China[J]. World Regional Studies, 2024, 33(4): 155-166.

图/表 11

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物流业主要能耗燃料	原煤	原油	汽油	煤油	柴油	燃料油	液化石油气	天然气
折算系数（kg标准煤/kg）	0.714	1.429	1.471	1.471	1.457	1.429	1.714	1.330
碳排放系数（t碳/t标准煤）	0.409	0.829	0.798	0.828	0.834	0.871	0.784	0.584

物流业主要能耗燃料	原煤	原油	汽油	煤油	柴油	燃料油	液化石油气	天然气
折算系数（kg标准煤/kg）	0.714	1.429	1.471	1.471	1.457	1.429	1.714	1.330
碳排放系数（t碳/t标准煤）	0.409	0.829	0.798	0.828	0.834	0.871	0.784	0.584

年份	Moran’s I	Z值	年份	Moran’s I	Z值
2006	0.365^***	3.710	2013	0.305^***	3.277
2007	0.383^***	3.831	2014	0.317^***	3.473
2008	0.369^***	3.739	2015	0.305^***	3.406
2009	0.382^***	3.874	2016	0.335^***	3.584
2010	0.388^***	3.922	2017	0.295^***	3.402
2011	0.363^***	3.692	2018	0.288^***	3.309
2012	0.328^***	3.460	2019	0.284^***	3.284

年份	Moran’s I	Z值	年份	Moran’s I	Z值
2006	0.365^***	3.710	2013	0.305^***	3.277
2007	0.383^***	3.831	2014	0.317^***	3.473
2008	0.369^***	3.739	2015	0.305^***	3.406
2009	0.382^***	3.874	2016	0.335^***	3.584
2010	0.388^***	3.922	2017	0.295^***	3.402
2011	0.363^***	3.692	2018	0.288^***	3.309
2012	0.328^***	3.460	2019	0.284^***	3.284

类型	HH_t+1	LH_t+1	LL_t+1	HL_t+1	类型	个数	比例
HH_t	Type 0(7,0.233)	Type Ⅱ(1,0.033)	Type Ⅰ(0,0.000)	Type Ⅲ(1,0.033)	Type 0	25	0.833
LH_t	Type Ⅱ(0,0.000)	Type 0(5,0.167)	Type Ⅲ(0,0.000)	Type Ⅰ(0,0.000)	Type Ⅰ	0	0.000
LL_t	Type Ⅰ(0,0.000)	Type Ⅲ(2,0.067)	Type 0(12,0.400)	Type Ⅱ(0,0.000)	Type Ⅱ	1	0.033
HL_t	Type Ⅲ(1,0.033)	Type Ⅰ(0,0.000)	Type Ⅱ(0,0.000)	Type 0(1,0.033)	Type Ⅲ	4	0.133