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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- 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
References
Andersen P, Petersen NC (1993) A procedure for ranking efficient units in data envelopment analysis. Manag Sci 39(10):1261–1264. https://doi.org/10.1287/mnsc.39.10.1261
Anselin L (1995) Local indicators of spatial association—LISA. Geogr Anal 27(2):93–115
Banomyong R, Thai VV, Yuen KF (2015) Assessing the national logistics system of Vietnam. Asian J Ship Logist 31(1):21–58. https://doi.org/10.1016/j.ajsl.2015.03.002
Bi G-B, Song W, Zhou P, Liang L (2014) Does environmental regulation affect energy efficiency in China’s thermal power generation? Empirical evidence from a slacks-based DEA model. Energy Policy 66:537–546. https://doi.org/10.1016/j.enpol.2013.10.056
Chang Y-T, Zhang N, Danao D, Zhang N (2013) Environmental efficiency analysis of transportation system in China: a non-radial DEA approach. Energy Policy 58:277–283. https://doi.org/10.1016/j.enpol.2013.03.011
Chen L, Jia G (2017) Environmental efficiency analysis of China’s regional industry: a data envelopment analysis (DEA) based approach. J Clean Prod 142:846–853. https://doi.org/10.1016/j.jclepro.2016.01.045
Chen CI, Yeh CH (2012) Re-examining location antecedents and pace of foreign direct investment: evidence from Taiwanese investments in China. J Bus Res 65(8):1171–1178. https://doi.org/10.1016/j.jbusres.2011.07.032
CHYXX (2019) China’s logistics industry in 2018. Retrieved from: http://www.chyxx.com/industry/201901/708851.html. 21 Jan 2019
Dai Y, Gao HO (2016) Energy consumption in China’s logistics industry: a decomposition analysis using the LMDI approach. Transp Res Part D Transp Environ 46:69–80. https://doi.org/10.1016/j.trd.2016.03.003
Dekker R, Bloemhof J, Mallidis I (2012) Operations research for green logistics – an overview of aspects, issues, contributions and challenges. European Journal of Operational Research 219(3):671–679. https://doi.org/10.1016/j.ejor.2011.11.010
Dornier P-P, Ernst R, Fender M, Kouvelis P (2008) Global operations and logistics: Text and cases. Wiley, Hoboken
Fan W, Xu M, Dong X, Wei H (2017) Considerable environmental impact of the rapid development of China’s express delivery industry. Resour Conserv Recycl 126:174–176. https://doi.org/10.1016/j.resconrec.2017.07.041
Goodchild MF (1992) Geographical information science. Int J Geogr Inf Syst 6(1):31–45. https://doi.org/10.1080/02693799208901893
Grant DB, Trautrims A, Wong CY (2017) Sustainable logistics and supply chain management: principles and practices for sustainable operations and management. Kogan Page Publishers, London
Guo X, Ren D, Shi J (2016) Carbon emissions, logistics volume and GDP in China: empirical analysis based on panel data model. Environ Sci Pollut Res 23(24):24758–24767. https://doi.org/10.1007/s11356-016-7615-z
Guo H, Zhao Y, Niu T, Tsui K-L (2017) Hong Kong Hospital Authority resource efficiency evaluation: via a novel DEA-Malmquist model and Tobit regression model. PLoS One 12(9):e0184211. https://doi.org/10.1371/journal.pone.0184211
Haggett P, Cliff AD, Frey A (1977) Locational analysis in human geography. Tijdschr Econ Soc Geogr 68(6)
Han L, Zhou W, Li W, Li L (2014) Impact of urbanization level on urban air quality: a case of fine particles (PM2. 5) in Chinese cities. Environ Pollut 194:163–170. https://doi.org/10.1016/j.envpol.2014.07.022
He B-J, Zhao D-X, Zhu J, Darko A, Gou Z-H (2018) Promoting and implementing urban sustainability in China: an integration of sustainable initiatives at different urban scales. Habitat Int 82:83–93. https://doi.org/10.1016/j.habitatint.2018.10.001
Heitz A, Dablanc L, Tavasszy LA (2017) Logistics sprawl in monocentric and polycentric metropolitan areas: the cases of Paris, France, and the Randstad, the Netherlands. Region 4(1):93–107
Hoque MA (2006) An optimal solution technique for the joint replenishment problem with storage and transport capacities and budget constraints. Eur J Oper Res 175(2):1033–1042. https://doi.org/10.1016/j.ejor.2005.06.022
Işıklar G, Alptekin E, Büyüközkan G (2007) Application of a hybrid intelligent decision support model in logistics outsourcing. Comput Oper Res 34(12):3701–3714. https://doi.org/10.1016/j.cor.2006.01.011
Isserman AM (1977) The location quotient approach to estimating regional economic impacts. J Am Inst Plann 43(1):33–41
Jain Palvia SC (2000) Global E-Commerce and Global Supply Chain Management. Taylor & Francis, Abingdon
Ji X, Wu J, Zhu Q (2016) Eco-design of transportation in sustainable supply chain management: a DEA-like method. Transp Res Part D: Transp Environ 48:451–459. https://doi.org/10.1016/j.trd.2015.08.007
Khan SAR, Qianli D, SongBo W, Zaman K, Zhang Y (2017) Environmental logistics performance indicators affecting per capita income and sectoral growth: evidence from a panel of selected global ranked logistics countries. Environ Sci Pollut Res 24(2):1518–1531. https://doi.org/10.1007/s11356-016-7916-2
Lee D-H, Dong M, Bian W (2010) The design of sustainable logistics network under uncertainty. Int J Prod Econ 128(1):159–166. https://doi.org/10.1016/j.ijpe.2010.06.009
Li Y-B, Liu S-X (2008) The forms of ecological logistics and its relationship under the globalization. Ecol Econ 4(3):290–298 in Chinese
Lin C-Y, Ho Y-H (2008) An empirical study on logistics service providers’ intention to adopt green innovations. J Technol Manag Innov 3(1):17–26 in Chinese
Liu C, Guan M (2017) Spatial evolution of Chinese logistics industry efficiency under low carbon constraints and it’s influencing factors. Sci Geogr Sin 37:1805–1814 in Chinese
Ma F, Li X, Sun Q, Liu F, Wang W, Bai L (2018a) Regional differences and spatial aggregation of sustainable transport efficiency: a case study of China. Sustainability 10(7):2399. https://doi.org/10.3390/su10072399
Ma X, Wang C, Yu Y, Li Y, Dong B, Zhang X, Niu X, Yang Q, Chen R, Li Y, Gu Y (2018b) Ecological efficiency in China and its influencing factors—a super-efficient SBM metafrontier-Malmquist-Tobit model study. Environ Sci Pollut Res 25(21):20880–20898. https://doi.org/10.1007/s11356-018-1949-7
Macharis C, Melo S, Woxenius J, Van Lier T (2014) Sustainable logistics. Emerald Group Publishing, Bingley
Markovits-Somogyi R, Bokor Z (2014) Assessing the logistics efficiency of European countries by using the DEA-PC methodology. Transport 29(2):137–145. https://doi.org/10.3846/16484142.2014.928787
McKinnon A (2010) Environmental sustainability. Green logistics: improving the environmental sustainability of logistics. London
Mentzer JT, Min S, Michelle Bobbitt L (2004) Toward a unified theory of logistics. Int J Phys Distrib Logist Manag 34(8):606–627. https://doi.org/10.1108/09600030410557758
Moschos D (1989) Export expansion, growth and the level of economic development: an empirical analysis. J Dev Econ 30(1):93–102. https://doi.org/10.1016/0304-3878(89)90052-7
Muñuzuri J, Van Duin J, Escudero A (2010) How efficient is city logistics? Estimating ecological footprints for urban freight deliveries. Procedia Soc Behav Sci 2(3):6165–6176. https://doi.org/10.1016/j.sbspro.2010.04.028
Murty KG, Liu J, Wan Y-w, Linn R (2005) A decision support system for operations in a container terminal. Decis Support Syst 39(3):309–332. https://doi.org/10.1016/j.dss.2003.11.002
NBSC, National Bureau of Statistics of the People’s Republic of China (2007–2016) China Statistics Yearbook. China Statistics Press, Beijing
NBSESD, National Statistics Bureau Energy Statistics Division (2007–2016) China Energy Statistics Yearbook. China Statistics Press, Beijing
Neto JQF, Bloemhof-Ruwaard JM, van Nunen JA, van Heck E (2008) Designing and evaluating sustainable logistics networks. Int J Prod Econ 111(2):195–208. https://doi.org/10.1016/j.ijpe.2006.10.014
Odland J (1988) Spatial autocorrelation, vol 9. Sage Publications, Newbury Park
Ogilvie T, Stein M, Griffin C (2002) Unattended package delivery cross-docking apparatus and method. Google Patents
Pender JL (1998) Population growth, agricultural intensification, induced innovation and natural resource sustainability: an application of neoclassical growth theory. Agric Econ 19(1-2):99–112. https://doi.org/10.1016/s0169-5150(98)00024-3
Piecyk M, Browne M, Whiteing A, McKinnon A (2015) Green logistics: improving the environmental sustainability of logistics. Kogan Page Publishers, London
Ramos TRP, Gomes MI, Barbosa-Póvoa AP (2014) Planning a sustainable reverse logistics system: balancing costs with environmental and social concerns. Omega 48:60–74. https://doi.org/10.1016/j.omega.2013.11.006
Reinhard S, Lovell CAK, Thijssen GJ (2000) Environmental efficiency with multiple environmentally detrimental variables; estimated with sfa and dea. Eur J Oper Res 121(2):287–303. https://doi.org/10.1016/s0377-2217(99)00218-0
Ruizhi P (2006) Dynamic evaluation of main sea ports in mainland China based on DEA model. Econ Res J 6:92–100 in Chinese
Russell DM, David S, Magnus B (2018) Sustainable logistics and supply chain management: a holistic view through the lens of the wicked problem. World Rev Intermodal Transp Res 7(1):36. https://doi.org/10.1504/WRITR.2018.089517
Sakai T, Kawamura K, Hyodo T (2015) Locational dynamics of logistics facilities: evidence from Tokyo. J Transp Geogr 46:10–19. https://doi.org/10.1016/j.jtrangeo.2015.05.003
Sakai T, Kawamura K, Hyodo T (2016) Logistics facility distribution in Tokyo metropolitan area: experiences and policy lessons. Transp Res Procedia 12:263–277. https://doi.org/10.1016/j.trpro.2016.02.064
Sbihi A, Eglese RW (2007) The relationship between vehicle routing & scheduling and green logistics-a literature survey. The Department of Management Science, Lancaster University, UK
Shi M (2018) Environmental pollution and measurement of low-carbon logistics efficiency in China’s logistics industry. Nat Environ Pollut Technol 17(4):1339–1346
Sun Q (2017) Research on the influencing factors of reverse logistics carbon footprint under sustainable development. Environ Sci Pollut Res 24(29):22790–22798. https://doi.org/10.1007/s11356-016-8140-9
Tang S, Wang W, Yan H, Hao G (2015) Low carbon logistics: reducing shipment frequency to cut carbon emissions. Int J Prod Econ 164:339–350. https://doi.org/10.1016/j.ijpe.2014.12.008
Testa F, Iraldo F (2010) Shadows and lights of gscm (green supply chain management): determinants and effects of these practices based on a multi-national study. J Clean Prod 18(10-11):953–962. https://doi.org/10.1016/j.jclepro.2010.03.005
Tone K (2001) A slacks-based measure of efficiency in data envelopment analysis. Eur J Oper Res 130(3):498–509. https://doi.org/10.1016/s0377-2217(99)00407-5
van Buren N, Demmers M, van der Heijden R, Witlox F (2016) Towards a circular economy: the role of Dutch logistics industries and governments. Sustainability 8(7):647. https://doi.org/10.3390/su8070647
van den Heuvel FP, De Langen PW, van Donselaar KH, Fransoo JC (2013) Spatial concentration and location dynamics in logistics: the case of a Dutch province. J Transp Geogr 28:39–48. https://doi.org/10.1016/j.jtrangeo.2012.10.001
Van den Heuvel FP, Rivera L, van Donselaar KH, de Jong A, Sheffi Y, de Langen PW, Fransoo JC (2014) Relationship between freight accessibility and logistics employment in US counties. Transp Res A Policy Pract 59:91–105. https://doi.org/10.1016/j.tra.2013.11.002
Wang Q, Zhang Y (2017) Study on the efficiency of logistics industry and its influencing factors in the“ core area” of the silk road economic belt. In: International Conference on Transformations and Innovations in Management (ICTIM 2017). Atlantis Press, Paris
Woo C, Chung Y, Chun D, Seo H, Hong S (2015) The static and dynamic environmental efficiency of renewable energy: a Malmquist index analysis of OECD countries. Renew Sust Energ Rev 47:367–376. https://doi.org/10.1016/j.rser.2015.03.070
Wu H-J, Dunn SC (1995) Environmentally responsible logistics systems. Int J Phys Distrib Logist Manag 25(2):20–38
Wu J, Zhu Q, Chu J, Liu H, Liang L (2016) Measuring energy and environmental efficiency of transportation systems in China based on a parallel DEA approach. Transp Res Part D: Transp Environ 48:460–472. https://doi.org/10.1108/09600039510083925
Xie X, Shao S, Lin B (2016) Exploring the driving forces and mitigation pathways of CO2 emissions in China’s petroleum refining and coking industry: 1995–2031. Appl Energy 184:1004–1015. https://doi.org/10.1016/j.apenergy.2016.06.008
Yang L, Wang K-L (2013) Regional differences of environmental efficiency of China’s energy utilization and environmental regulation cost based on provincial panel data and DEA method. Math Comput Model 58(5-6):1074–1083. https://doi.org/10.1016/j.mcm.2012.04.004
Yu Z, Wu P (2010) An empirical study on the efficiency of China’s logistics industry and its factors. Ind Econ Res (1):65–71 in Chinese
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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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