黄河流域物流业全要素碳生产率测度及影响因素——基于能源禀赋差异视角

doi:10.12118/j.issn.1000-6060.2025.300

摘要/Abstract

摘要：

研究黄河流域能源禀赋差异下的物流业全要素碳生产率（简称碳生产率）对于明晰该区域降碳减排路径具有重要意义。基于能源禀赋度划分黄河流域为能源富集区及一般区，利用超效率SBM模型与ML指数探究黄河流域碳生产率的静态效率值和动态效率指数并通过回归分析明确各影响因素对碳生产率的影响机理。结果表明：（1）碳生产率呈现区域异质性，能源富集区碳生产率较高且增长较快，技术效率提升显著；能源一般区整体增长但内部效率分化明显。（2）效率动态特征，能源富集区与能源一般区碳生产率的技术效率与技术进步呈现分化特征，能源富集区技术效率领先能源一般区，能源一般区中四川、宁夏的技术提升较为显著，山东的技术进步动力不足。（3）驱动机制呈现差异化，能源价格仅在能源富集区呈正面影响，而在能源一般区产生抑制；能源禀赋度仅对能源一般区有正向作用，不同影响因素在不同区域内产生差异性。

关键词: 物流业, 全要素碳生产率, 超效率SBM模型, 黄河流域

Abstract:

The study on the total factor carbon productivity of the logistics industry in the Yellow River Basin, based on differences in energy endowment, is crucial for understanding the regional strategies for carbon reduction. The Yellow River Basin is categorized into energy-rich and energy-general areas, based on the level of energy endowment. This study examines the static and dynamic efficiency indices of carbon productivity using the super-efficient SBM model and the Modified Luenberger index. In addition, it clarifies how different factors influence carbon productivity through regression analysis. The results indicate that (1) Carbon productivity varies across regions. Energy-rich areas experience greater and faster increases in carbon productivity, driven by significant improvements in technical efficiency. In contrast, energy-general areas also show overall growth but display distinctly different internal efficiency. (2) Regarding efficiency dynamics, the technical efficiency and technological progress of carbon productivity vary between energy-rich and energy-poor areas. The technical efficiency of energy-rich regions surpasses that of the energy-general area. Within the energy-general area, technological enhancement is more prominent in Sichuan and Ningxia, while that in Shandong is inadequate. (3) The driving mechanisms vary between regions. Energy prices positively influence the energy-rich region but not in the energy-general region. Conversely, energy endowment only benefits the energy-general region. This indicates that the influence of different factors varies considerably among regions.

Key words: logistics industry, total factor carbon productivity, super-efficiency SBM model, Yellow River Basin

郝晓燕, 李玥蓉, 吴月. 黄河流域物流业全要素碳生产率测度及影响因素——基于能源禀赋差异视角[J]. 干旱区地理, 2026, 49(4): 756-768.

HAO Xiaoyan, LI Yuerong, WU Yue. Measurement of total factor carbon productivity of the logistics industry in the Yellow River Basin and its influencing factors: A perspective based on energy endowment differences[J]. Arid Land Geography, 2026, 49(4): 756-768.

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一级指标	二级指标	指标解释
能源因素	能源禀赋度	煤、石油、天然气的产量折算为标准煤之后占全国的比重
	能源价格	燃料、动力类工业生产者购进价格指数代替^[29]
	能源消费结构	能源消费结构主要指物流业各种能源消费的比重，用能源结构系数表示
	能源效率	能源效率用物流业能源消耗量除以物流业增加值表示
经济因素	物流专业化水平	物流专业化水平用物流业增加值除以当年GDP水平表示
经济因素	经济规模	地区生产总值表示
其他	人口规模	人口规模指各省区的年末人口数

年份	能源富集区			能源一般区
年份	山西	内蒙古	陕西	青海	四川	甘肃	宁夏	河南	山东
2013	0.487	0.615	0.596	0.297	1.028	0.481	1.086	0.800	0.839
2014	0.510	0.708	0.597	0.308	0.481	0.343	1.083	1.152	1.034
2015	0.584	0.718	0.611	0.336	1.004	0.441	0.822	1.048	1.053
2016	0.555	0.783	0.676	0.283	0.445	0.295	0.699	1.067	1.075
2017	1.376	1.003	0.685	0.241	0.420	0.405	0.605	1.071	1.128
2018	1.107	1.108	0.568	0.236	0.380	0.401	0.550	1.070	0.861
2019	1.053	1.091	0.600	0.250	0.388	0.401	0.627	1.082	1.040
2020	1.006	1.087	1.048	0.249	0.355	0.365	0.662	1.003	1.031
2021	0.826	1.141	1.005	0.234	0.304	0.346	0.575	0.770	1.103
2022	0.717	1.161	1.044	0.237	0.301	0.392	0.551	0.783	1.110
均值	0.822	0.941	0.743	0.267	0.511	0.387	0.726	0.985	1.028

年份	能源富集区			能源一般区
年份	山西	内蒙古	陕西	青海	四川	甘肃	宁夏	河南	山东
2013—2014	1.063	1.103	1.050	1.037	1.063	0.698	0.978	1.444	0.996
2014—2015	1.090	1.076	0.988	1.136	2.097	1.293	0.929	0.928	1.009
2015—2016	1.041	1.293	1.238	0.899	0.669	0.716	1.060	1.097	1.076
2016—2017	2.472	1.342	1.272	0.957	1.035	1.491	0.957	1.019	1.092
2017—2018	0.930	1.295	1.009	1.161	1.119	1.189	1.107	1.154	0.998
2018—2019	0.847	0.986	1.140	1.106	1.081	1.055	1.184	1.063	1.247
2019—2020	0.974	1.006	1.742	0.947	0.887	0.871	1.059	0.904	0.981
2020—2021	1.057	1.157	1.361	1.201	1.110	1.227	1.053	1.077	1.277
2021—2022	0.920	1.001	1.304	1.076	1.057	1.201	1.019	1.201	1.053
均值	1.155	1.140	1.234	1.058	1.124	1.082	1.039	1.098	1.081

年份	能源富集区		能源一般区
年份	MLEC	MLTC	MLEC	MLTC
2013—2014	1.067	1.007	0.981	1.174
2014—2015	1.061	0.993	1.192	1.048
2015—2016	1.049	1.133	0.807	1.171
2016—2017	1.591	1.100	1.015	1.078
2017—2018	0.913	1.181	0.924	1.218
2018—2019	0.997	0.991	1.074	1.046
2019—2020	1.232	1.009	0.966	0.974
2020—2021	0.943	1.270	0.908	1.280
2021—2022	0.975	1.100	1.020	1.080
均值	1.092	1.087	0.987	1.119

指数	能源富集区			能源一般区
指数	山西	内蒙古	陕西	青海	四川	甘肃	宁夏	河南	山东
MLEC	1.114	1.077	1.086	0.979	0.959	1.002	0.934	1.010	1.040
MLTC	1.053	1.058	1.151	1.084	1.279	1.079	1.122	1.098	1.052