Evolution characteristics and convergence analysis of carbon emission efficiency of provincial logistics in China

doi:10.3969/j.issn.1004-9479.2024.04.20220192

Abstract

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

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

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.

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.

Figures/Tables 11

References 34

1	丁俊发. 改革开放40年中国物流业发展与展望. 中国流通经济, 2018, 32(4): 3-17.
	DING J. The development of China's logistics industry during the past 40 years of reform and opening up and the outlook of that. China Business and Market, 2018, 32(4): 3-17.
2	刘丙泉, 程凯, 马占新. 城镇化对物流业碳排放变动影响研究. 中国人口·资源与环境, 2016, 26(3): 54-60.
	LIU B, CHENG K, MA Z. Research on the impacts of urbanization on logistics carbon emission changes. China Population, Resources and Environment, 2016, 26(3): 54-60.
3	李子晨. 未来5年实现碳效率提升35%. 国际商报, 2021-10-25(5).
	LI Z. Achieve 35% improvement in carbon efficiency in the next five years. International Business, 2021-10-25(5).
4	ABUKHADER S, JÖNSON G. Logistics and the environment: Is it an established subject? International Journal of Logistics Research and Applications, 2004, 7(2): 137-149.
5	HEROLD D, LEE K. Carbon management in the logistics and transportation sector: An overview and new research directions. Carbon Management, 2017, 8(1): 79-97.
6	GUO X, WANG D. Analysis of the spatial relevance and influencing factors of carbon emissions in the logistics industry from China. Environmental Science and Pollution Research, 2022, 29(2): 2672-2684.
7	WANG J, LIM M, TSENG M, et al. Promoting low carbon agenda in the urban logistics network distribution system. Journal of Cleaner Production, 2019, 211: 146-160.
8	LIU Z, GUAN D, WEI W, et al. Reduced carbon emission estimates from fossil fuel combustion and cement production in China. Nature, 2015, 524(7565): 335-338.
9	GAO P, YUE S, CHEN H. Carbon emission efficiency of China's industry sectors: From the perspective of embodied carbon emissions. Journal of Cleaner Production, 2021, 283: 1-9.
10	邵海琴, 王兆峰. 中国交通碳排放效率的空间关联网络结构及其影响因素. 中国人口·资源与环境, 2021, 31(4): 32-41.
	SHAO H, WANG Z. Spatial network structure of transportation carbon emissions efficiency in China and its influencing factors. China Population, Resources and Environment, 2021, 31(4): 32-41.
11	王凯, 张淑文, 甘畅, 等. 中国旅游业碳排放效率的空间网络结构及其效应研究. 地理科学, 2020, 40(3): 344-353.
	WANG K, ZHANG S, GAN C, et al. Spatial network structure of carbon emission efficiency of tourism industry and its effects in China. Scientia Geographica Sinica, 2020, 40(3): 344-353.
12	DU Q, DENG Y, ZHOU J, et al. Spatial spillover effect of carbon emission efficiency in the construction industry of China. Environmental Science and Pollution Research, 2022, 29(2): 2466-2479.
13	WANG Q, HANG Y, SU B, et al. Contributions to sector-level carbon intensity change: An integrated decomposition analysis. Energy Economics, 2018, 70: 12-25.
14	ZHANG Z, QU J, ZENG J. A quantitative comparison and analysis on the assessment indicators of greenhouse gases emission. Journal of Geographical Sciences, 2008, 18(4): 387-399.
15	程占红, 徐娇. 五台山景区酒店碳排放效率的典范对应分析. 地理研究, 2018, 37(3): 577-592.
	CHENG Z, XU J. Canonical correspondence analysis of hotels' carbon emission efficiency in Wutai Mountain scenic area. Geographical Research, 2018, 37(3): 577-592.
16	ZHOU Z, LIU C, ZENG X, et al. Carbon emission performance evaluation and allocation in Chinese cities. Journal of Cleaner Production, 2018, 172: 1254-1272.
17	李晨, 冯伟, 邵桂兰. 中国省域渔业全要素碳排放效率时空分异. 经济地理, 2018, 38(5): 179-187.
	LI C, FENG W, SHAO G. Spatio-temporal difference of total carbon emission efficiency of fishery in China. Economic Geography, 2018, 38(5): 179-187.
18	刘承良, 管明明. 低碳约束下中国物流业效率的空间演化及影响因素. 地理科学, 2017, 37(12): 1805-1814.
	LIU C, GUAN M. Spatial evolution of Chinese logistics industry efficiency under low carbon constraints and it's influencing factors. Scientia Geographica Sinica, 2017, 37(12): 1805-1814.
19	WANG K, WU M, SUN Y, et al. Resource abundance, industrial structure, and regional carbon emissions efficiency in China. Resources Policy, 2019, 60: 203-214.
20	王鑫静, 程钰. 城镇化对碳排放效率的影响机制研究——基于全球118个国家面板数据的实证分析. 世界地理研究, 2020, 29(3): 503-511.
	WANG X, CHENG Y. Research on the influencing mechanism of urbanization on carbon emission efficiency--Based on an empirical study of 118 countries. World Regional Studies, 2020, 29(3):503-511.
21	蔺雪芹, 边宇, 王岱. 京津冀地区工业碳排放效率时空演化特征及影响因素. 经济地理, 2021, 41(6): 187-195.
	LIN X, BIAN Y, WANG D. Spatiotemporal evolution characteristics and influencing factors of industrial carbon emission efficiency in Beijing-Tianjin-Hebei region. Economic Geography, 2021, 41(6): 187-195.
22	ZHU J, SUN H, ZHOU D, et al. Carbon emission efficiency of thermal power in different regions of China and spatial correlations. Mitigation and Adaptation Strategies for Global Change, 2020, 25(7): 1221-1242.
23	王少剑,高爽,黄永源,等.基于超效率SBM模型的中国城市碳排放绩效时空演变格局及预测.地理学报,2020,75(6): 1316-1330.
	WANG S, GAO S, HUANG Y, et al. Spatio-temporal evolution and trend prediction of urban carbon emission performance in China based on super-efficiency SBM Model. Acta Geographica Sinica, 2020, 75(6): 1316-1330.
24	方叶林, 黄震方, 李经龙, 等. 中国省域旅游经济增长的时空跃迁及其趋同研究. 地理科学, 2018, 38(10): 1616-1623.
	FANG Y, HUANG Z, LI J, et al. Space-time transition of tourism economic growth and its convergence in Chinese mainland. Scientia Geographica Sinica, 2018, 38(10): 1616-1623.
25	WEI Y, LI Y, WU M, et al. The decomposition of total-factor CO₂ emission efficiency of 97 contracting countries in Paris Agreement. Energy Economics, 2019, 78: 365-378.
26	贺三维, 张臻, 甘杨旸. 隐性经济视角下异质性环境规制对雾霾污染的时空效应. 经济地理, 2022, 42(4): 178-189.
	HE S, ZHANG Z, GAN Y. Spatio-temporal effect of environmental regulations on haze pollution from the perspective of implicit economy. Economic Geography, 2022, 42(4): 178-189.
27	REY S. Spatial empirics for economic growth and convergence. Geographical Analysis, 2001, 33(3): 195-214.
28	REY S, JANIKAS M. STARS: Space-time analysis of regional systems. Geographical Analysis, 2006, 38(1): 67-86.
29	PÁEZ J. Progress in Spatial Analysis: Theory, Computation and Thematic Applications. Heidelberg: Springer, 2010.
30	王许亮, 王恕立, 滕泽伟. 中国服务业碳生产率的空间收敛性研究. 中国人口·资源与环境, 2020, 30(2): 70-79.
	WANG X, WANG S, TENG Z. Research on spatial convergence of carbon productivity in China's service industry. China Population, Resources and Environment, 2020, 30(2): 70-79.
31	YANG G, ZHANG F, ZHANG F, et al. Spatiotemporal changes in efficiency and influencing factors of China's industrial carbon emissions. Environmental Science and Pollution Research, 2021, 28(27): 36288-36302.
32	SHAN Y, GUAN D, LIU J, et al. Methodology and applications of city level CO₂ emission accounts in China. Journal of Cleaner Production, 2017, 161: 1215-1225.
33	SHAN Y, GUAN D, ZHENG H, et al. China CO₂ emission accounts 1997-2015. Scientific Data, 2018, 5(1): 1-14.
34	YANG J, TANG L, MI Z, et al. Carbon emissions performance in logistics at the city level. Journal of Cleaner Production, 2019, 231: 1258-1266.

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