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

Abstract

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

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.

Figures/Tables 13

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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

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

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解释变量	黄河流域整体	能源富集区		能源一般区
解释变量	初始回归	初始回归	进一步回归	初始回归	进一步回归
能源禀赋度	0.22^**	0.39	0.50	0.54^***	0.48^***
能源禀赋度	（0.014）	（0.266）	（0.110）	（0.000）	（0.000）
能源价格	0.02	0.75	0.74^*	-0.65^*	-0.44
能源价格	（0.944）	（0.235）	（0.088）	（0.071）	（0.134）
能源消费结构	-0.33	0.29	0.05	-1.65^**	-0.33
能源消费结构	（0.383）	（0.687）	（0.903）	（0.006）	（0.551）
能源效率	0.66^***	0.65	0.11^**	0.52^**	0.02
能源效率	（0.000）	（0.286）	（0.031）	（0.004）	（0.721）
物流专业化水平	-0.56^***	-0.59	-	-0.45^**	-
物流专业化水平	（0.000）	（0.330）		（0.010）
经济规模	-0.76^***	-0.72	-	-0.09	-
经济规模	（0.000）	（0.497）		（0.590）
人口规模	0.83^**	-0.16	-	-0.04	-
人口规模	（0.002）	（0.765）		（0.780）
常数	-0.86	3.36	-2.84	4.88^**	3.58^**
常数	（0.633）	（0.597）	（0.212）	（0.001）	（0.007）
总体R²	0.4781	0.4510	0.4119	0.8413	0.8063
样本观测数	86	29 3		57 6
组数	9	29 3		57 6

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