A New Solution for City Distribution to Achieve Environmental Benefits within the Trend of Green Logistics: A Case Study in China

Green logistics has become a consensus and an important method to achieve sustainable development in industrial activities. However, the traditional direct distribution mode has high carbon emissions, an uncertain delivery time, and a low delivery efficiency. Uncoordinated resource allocations and unreasonable network layouts of terminal distributions have shackled green development within the express delivery industry. Considering the trend of green logistics, this study innovatively proposes a comprehensive and environmentally friendly mode for city distribution based on end crowdsourcing service stations (ECSSs). This study also adopts node centrality indices of complex network theory to evaluate the node importance of existing terminal distribution outlets. The comprehensive weights of the indices are obtained via the three-scale AHP (Analytic Hierarchy Process) and TOPSIS (Technology for Order Preference by Similarity to an Ideal Solution) methods to identify the candidate nodes for ECSSs. Finally, a location model is built to determine the optimal location to establish the ECSSs. A real-world case study was conducted to provide the location scheme of ECSSs in Beijing, China. Environmental benefits as well as economic and social benefits can be substantially achieved through the implementation of the new mode. The results show that carbon emissions can be reduced by 23.79–28.49% for the end of the distribution, 16.27–16.35% for the front-end, and approximately 17% for the entire distribution. Additionally, the loading rate of vans for the front-end of the distribution can be improved by 15.77%.

[1]  Vittaldas V. Prabhu,et al.  Smart logistics: distributed control of green crowdsourced parcel services , 2016 .

[2]  Bo Zou,et al.  Design and modeling of a crowdsource-enabled system for urban parcel relay and delivery , 2017 .

[3]  F. Arcelus,et al.  Green logistics at Eroski: A case study , 2011 .

[4]  Carlos Carrascosa,et al.  A Crowdsourcing Approach for Sustainable Last Mile Delivery , 2018, Sustainability.

[5]  Sushil Kumar,et al.  Analytic hierarchy process: An overview of applications , 2006, Eur. J. Oper. Res..

[6]  Jagjit Singh Srai,et al.  Hierarchical modelling of Last Mile logistic distribution system , 2014 .

[7]  S. A. R. Khan,et al.  The nexus between carbon emissions, poverty, economic growth, and logistics operations-empirical evidence from southeast Asian countries , 2019, Environmental Science and Pollution Research.

[8]  C. Macharis,et al.  Crowd logistics: an opportunity for more sustainable urban freight transport? , 2017, European Transport Research Review.

[9]  Si Chen,et al.  A Novel Intensive Distribution Logistics Network Design and Profit Allocation Problem considering Sharing Economy , 2018, Complex..

[10]  Yikun Zhang,et al.  Analysis on the location of green logistics park based on heuristic algorithm , 2018 .

[11]  Hing Kai Chan,et al.  A hierarchical fuzzy TOPSIS approach to assess improvement areas when implementing green supply chain initiatives , 2013 .

[12]  A. Barten Mathematics for econometrics: Phoebus J. DHRYMES Springer, New York, 1978, viii + 136 pages, DM 42.00, US $ 23.10 , 1980 .

[13]  Leonardo Caggiani,et al.  A Sustainable Crowdsourced Delivery System to Foster Free-Floating Bike-Sharing , 2019, Sustainability.

[14]  Jairo R. Montoya-Torres,et al.  On the impact of collaborative strategies for goods delivery in city logistics , 2016 .

[15]  Cathy Macharis,et al.  Shipping outside the box. Environmental impact and stakeholder analysis of a crowd logistics platform in Belgium , 2018, Journal of Cleaner Production.

[16]  Miguel Andres Figliozzi,et al.  A Survey of China’s Logistics Industry and the Impacts of Transport Delays on Importers and Exporters , 2010 .

[17]  Qing He,et al.  Crowdsourcing the last mile delivery of online orders by exploiting the social networks of retail store customers , 2017 .

[18]  Ji-dong Guo,et al.  Environmental impact assessment for city logistics distribution systems , 2017 .

[19]  Ming Zhou,et al.  Capacitated multi-modal network flow models for minimizing total operational cost and CO2e emission , 2018, Comput. Ind. Eng..

[20]  Miroslaw J. Skibniewski,et al.  Evaluation of Advanced Construction Technology with AHP Method , 1992 .

[21]  Hua Yu,et al.  The node importance in actual complex networks based on a multi-attribute ranking method , 2015, Knowl. Based Syst..

[22]  Jung-Tang Hsueh,et al.  Integrating the AHP and TOPSIS decision processes for evaluating the optimal collection strategy in reverse logistic for the TPI , 2017 .

[23]  Abdelmohsen A. Nassani,et al.  Management of green transportation: an evidence-based approach , 2019, Environmental Science and Pollution Research.

[24]  Nursel Öztürk,et al.  A memory structure adapted simulated annealing algorithm for a green vehicle routing problem , 2015, Environmental Science and Pollution Research.

[25]  Jianxin Wang,et al.  Promoting low carbon agenda in the urban logistics network distribution system , 2019, Journal of Cleaner Production.

[26]  Qiang Sun Empirical research on coordination evaluation and sustainable development mechanism of regional logistics and new-type urbanization: a panel data analysis from 2000 to 2015 for Liaoning Province in China , 2017, Environmental Science and Pollution Research.

[27]  Xu Wang,et al.  Study on relationship between green logistics activity and logistics performance , 2019, Cluster Computing.

[28]  Samir K. Srivastava,et al.  Green Supply-Chain Management: A State-of-the-Art Literature Review , 2007 .

[29]  Marialisa Nigro,et al.  Sustainable crowdshipping using public transport: a case study evaluation in Rome , 2018 .

[30]  Xiaopeng Guo,et al.  Carbon emissions, logistics volume and GDP in China: empirical analysis based on panel data model , 2016, Environmental Science and Pollution Research.

[31]  K. Bi,et al.  Reducing carbon emissions from collaborative distribution: a case study of urban express in China , 2020, Environmental Science and Pollution Research.

[32]  Besma Talbi,et al.  CO2 emissions reduction in road transport sector in Tunisia , 2017 .

[33]  Michael Lettenmeier,et al.  Transport reduction by crowdsourced deliveries – a library case in Finland , 2016 .

[34]  Jun Zhang,et al.  How to Choose “Last Mile” Delivery Modes for E-Fulfillment , 2014 .

[35]  Danish,et al.  Modeling the impact of transport energy consumption on CO2 emission in Pakistan: Evidence from ARDL approach , 2018, Environmental Science and Pollution Research.

[36]  M. Roccotelli,et al.  A Review of Last Mile Logistics Innovations in an Externalities Cost Reduction Vision , 2018 .

[37]  Jian Xue,et al.  Location selection of intra-city distribution hubs in the metro-integrated logistics system , 2018, Tunnelling and Underground Space Technology.

[38]  Dong Qianli,et al.  Does national scale economic and environmental indicators spur logistics performance? Evidence from UK , 2017, Environmental Science and Pollution Research.

[39]  Hua Yang,et al.  Insight to the express transport network , 2009, Comput. Phys. Commun..

[40]  Alfred L. Guiffrida,et al.  Carbon emissions comparison of last mile delivery versus customer pickup , 2014 .

[41]  Boaz Golany,et al.  A parcel locker network as a solution to the logistics last mile problem , 2018, Int. J. Prod. Res..

[42]  M. Romance,et al.  Optimal distributions for multiplex logistic networks. , 2016, Chaos.

[43]  Atef Saad Alshehry,et al.  Study of the environmental Kuznets curve for transport carbon dioxide emissions in Saudi Arabia , 2017 .

[44]  Leo G. Kroon,et al.  Crowdsourced Delivery - A Dynamic Pickup and Delivery Problem with Ad Hoc Drivers , 2016, Transp. Sci..

[45]  Wilfried Sihn,et al.  Structural concepts for horizontal cooperation to increase efficiency in logistics , 2011 .

[46]  Weiguo Fan,et al.  Considerable environmental impact of the rapid development of China's express delivery industry , 2017 .

[47]  L. Švadlenka,et al.  A Proposal for a Decision-Making Tool in Third-Party Logistics (3PL) Provider Selection Based on Multi-Criteria Analysis and the Fuzzy Approach , 2019, Sustainability.

[48]  Wei Zhang,et al.  What influences the effectiveness of green logistics policies? A grounded theory analysis. , 2020, The Science of the total environment.

[49]  Rong-juan Luo,et al.  A probability guided evolutionary algorithm for multi-objective green express cabinet assignment in urban last-mile logistics , 2018, Int. J. Prod. Res..

[50]  Yuval Shavitt,et al.  A model of Internet topology using k-shell decomposition , 2007, Proceedings of the National Academy of Sciences.

[51]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[52]  Jianming Yao,et al.  Identifying the driving forces of CO2 emissions of China’s transport sector from temporal and spatial decomposition perspectives , 2019, Environmental Science and Pollution Research.

[53]  Qing Liu,et al.  Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions , 2016 .