Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/228946
Authors: 
Siragusa, Chiara
Mangiaracina, Riccardo
Tumino, Angela
Year of Publication: 
2020
Citation: 
[Editor:] Jahn, Carlos [Editor:] Kersten, Wolfgang [Editor:] Ringle, Christian M. [Title:] Data Science in Maritime and City Logistics: Data-driven Solutions for Logistics and Sustainability. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 30 [ISBN:] 978-3-7531-2347-9 [Year:] 2020 [Pages:] 71-95
Abstract: 
Purpose: B2C e-commerce is growing worldwide, and a major concern regards its environmental sustainability. Some studies compared the environmental impact of the online and offline purchasing processes, considering the shopping made at one store at a time (e.g. a book bought in a physical store or on internet). This work aims instead to investigate the environmental impact of multi-item shopping. Methodology: The environmental impact of the purchasing processes is evaluated in terms of CO2e emissions. The model, based on an activity-based approach, allows to assess the environmental impact of the online and offline shopping in the main industries (fashion, consumer electronics, books, grocery) considering (i) one purchase at a time and (ii) multiple purchases in different stores, either online or offline. Findings: If comparing the same purchase made in the online and offline channels, for a specific industry, the e-commerce case generates lower emissions - even if the results depends on many variables, e.g. customer density, mean of transport. Results overturn when, in the same offline shopping trip, the customer buys in more than one store. Originality: The main contribution is the multi-item approach while evaluating the environmental sustainability of the purchasing processes, which is not tackled by literature in this regard. This allows to make significant considerations on sustainability from a logistics perspective.
Subjects: 
Logistics
Industry 4.0
Supply Chain Management
Sustainability
City Logistics
Maritime Logistics
Data Science
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-sa/4.0/
Document Type: 
Conference Paper

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