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A panel analysis of the sustainability of logistics industry in China: based on non-radial slacks-based method

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Abstract

Previous studies have primarily targeted at positive causal linkages between the logistics industry and economic benefits, resulting in biased findings without the consideration of undesirable social and environmental problems. Therefore, this paper aims to develop a holistic approach to the assessment of logistics efficiency, through considering comprehensive inputs and desirable and undesirable outputs. In specific, contextualized in China, this paper comprehensively examined the spatiotemporal variations of China logistics efficiency and further investigated the impact of some exogenous factors. Results indicate that the overall logistics efficiency of China was low, but temporally showed a trend of increase. Spatially, the logistics efficiency followed the pattern of Eastern > Central > Western > Northeastern. Moreover, for the spatial interaction among adjacent provinces, there occurred high–high patterns in the Eastern, and low–low aggregation in the Western and Northeastern regions. However, along with time, the spatial interaction among adjacent provinces was weakening. For exogenous factors, level of economic development, urbanization level, utilization rate of logistics resources, and location advantage had a significant positive impact on SLE, while the effect of labor quality was not significant. Overall, this paper enriches the theoretical understandings of sustainable logistics efficiency evaluation and unbiasedly inform central and local governments with approaches to optimizing logistics efficiency.

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Abbreviations

SLE:

Sustainable logistics efficiency

CAGR:

Compound annual growth rate

SFA:

Stochastic frontier analysis

DEA:

Data envelopment analysis

TFP:

Total factor productivity

SBM-DEA:

A slacks-based measure data envelopment analysis

super-SBM-DEA:

Super-efficiency DEA with non-radial slacks-based measures

LISA:

Local index spatial autocorrelation

DMU:

Decision-making unit

LIFA:

Logistics investment in fixed assets

LEC:

Logistics energy consumption

NLE:

Number of logistics employees

FTL:

Freight turnover of the logistics industry

LLL:

Length of logistics line

LUL:

Land use of logistics

LAPL:

Logistic accident property loss

LED:

Level of economic development

UL:

Urbanization level

LQ:

Labor quality

URLR:

Utilization rate of logistics resource

LA:

Location advantage

GDP:

Gross domestic product

AVL:

Added value of the logistics industry

CDE:

Carbon dioxide emission

TO:

Trucks ownership

FL:

Freight of logistics industry

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Funding

This research was financially supported by the Humanities and Social Sciences Foundation, Ministry of Education, the People’s Republic of China (grant no. 16YJC630053), Shaanxi Social Science Foundation, China (grant no. 2017S019), and the Fundamental Research Funds for the Central Universities of Ministry of Education of China (no. 300102239605).

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Correspondence to Lingling Tan.

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Responsible editor: Nicholas Apergis

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Tan, L., Wu, Q., Li, Q. et al. A panel analysis of the sustainability of logistics industry in China: based on non-radial slacks-based method. Environ Sci Pollut Res 26, 21948–21963 (2019). https://doi.org/10.1007/s11356-019-05481-8

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  • DOI: https://doi.org/10.1007/s11356-019-05481-8

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