Online Shopping

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Online Shopping

Positive effects vs. Rebound Effects

Amalia Paulsson

Digitalization and the Rebound Effect – Seminar HS2019 ETH Zurich

1

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Retail e-commerce sales worldwide from 2014 to 2023(bn $)

1336 1548 1845

2382

2982

3535

4206

4927

5695

6542

0 1000 2000 3000 4000 5000 6000 7000

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023

Salesin billion U.S. dollars

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Regional sales share of global e- commerce

0 20 40 60 80 100 120

2010 2017 2021

Asia-Pacific North America Western Europe Eastern Europé Latin America Middle East & Africa

3

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Purpose for this study

• Examine three methodologies for comparing traditional retail and b2c e-commerce

• Find out if there is a rigorous conclusion on which alternative is the most environment friendly

• Identify major emission drivers and motivate further research

5

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Background - Life-cycle Assessment (LCA)

•

Model for calculating footprint

•

Goes through product phases

• Raw Materials

• Manufacturing

• Distribution

• Usage

• Recycling/Landfill

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Presentation outline

• Case study 1: LCA with block diagrams

• Case study 2: Energy use in distribution network

• Case study 3: Alternative delivery models

• Traditional retail vs. E-commerce – which is most environment friendly?

• Sensitivity analysis & Future possibilities

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Life Cycle Assessment

• Sivaraman et al. 2003: case study on DVD rental in Ann Arbor, Michigan.

• Comparing rental networks: DVD-rental through traditional retail versus e- commerce.

• Assumptions

• Customers always rent three DVDs at one time

• Same consumer behavior regardless of the situation of purchase

• Limited routes

• Proposes modelling the LCA with block diagrams

LCA Distribution network Delivery models Trad vs. E-com Future possibilities

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Traditional network

9

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E-commerce

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Williams and Tagami 2003: Case Study on Book Retail

• Case study on book retail in Japan

• Focuses solely on energy use

• Examines four factors in the distribution network

•

Building Energy & Electricity Consumption

•

Energy use in Packaging

•

Energy use in Personal Transport

•

Energy use in Shipping and Courier Services

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Factor Trad.

retail E-com Building

energy x x

Packaging

energy x x

Personal

transport x

Shipping x x

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Building Energy & Electricity Consumption

• Centers, warehouses, local store & home of the customer

• Simplification: one distribution center only

Boils down the comparison to the bookstore/ customer’s residence

• The store consumed 1.1 MJ per book

• Online purchase consumed 0.95 MJ

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Energy use in Production of Packaging

• Life cycle analysis for material types

• Weighting factors for each using step

• Dimensions (centimeters

²

)

• Weight/area (grams/ centimeters

²

)

• Production energy for material type (MJ/grams)

• 3.9 MJ/online purchase & 0.8 MJ/purchase in traditional store

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Energy Use in Personal Transport

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Personal Transport Energy per book

𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑇𝑇𝐶𝐶𝑇𝑇𝐶𝐶𝐶𝐶𝑇𝑇𝐶𝐶𝐶𝐶𝑇𝑇 𝑈𝑈𝐶𝐶𝐶𝐶 𝑀𝑀𝑀𝑀 𝐵𝐵𝐶𝐶𝐶𝐶𝐵𝐵 =

�

𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚=𝑎𝑎𝑎𝑎𝑎𝑎𝑚𝑚,𝑎𝑎𝑡𝑡𝑎𝑎𝑡𝑡𝑡𝑡,𝑏𝑏𝑎𝑎𝑚𝑚

𝑃𝑃𝐶𝐶𝐶𝐶 𝐶𝐶𝑇𝑇𝑇𝑇𝐶𝐶𝑇𝑇𝑇𝑇 𝐸𝐸𝐶𝐶𝐶𝐶𝐶𝐶𝐸𝐸𝐸𝐸 𝑈𝑈𝐶𝐶𝐶𝐶 𝑓𝑓𝐶𝐶𝐶𝐶 𝑀𝑀𝐶𝐶𝑀𝑀𝐶𝐶 𝑀𝑀𝑀𝑀

𝑃𝑃𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 × 𝑅𝑅𝐶𝐶𝐸𝐸𝐶𝐶𝐶𝐶𝐶𝐶𝑇𝑇𝑅𝑅 𝐸𝐸𝐸𝐸𝑇𝑇𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶𝑇𝑇𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶 𝑀𝑀𝐶𝐶𝑀𝑀𝐶𝐶[𝑐𝑐𝑇𝑇𝑇𝑇𝐶𝐶𝑇𝑇𝑇𝑇𝑅𝑅]

𝑁𝑁𝑇𝑇𝑇𝑇𝐶𝐶𝐶𝐶𝐶𝐶𝑇𝑇𝑅𝑅 𝐴𝐴𝐴𝐴𝐶𝐶𝐶𝐶𝑇𝑇𝐸𝐸𝐶𝐶[𝑐𝑐𝑇𝑇𝑇𝑇𝐶𝐶𝑇𝑇𝑇𝑇𝑅𝑅] × 𝑆𝑆𝑆𝐶𝐶𝑇𝑇𝑇𝑇𝐶𝐶𝐶𝐶𝐸𝐸 𝑇𝑇𝐶𝐶𝐶𝐶𝑇𝑇𝐶𝐶[𝐵𝐵𝐶𝐶]

𝑇𝑇𝐶𝐶𝑇𝑇𝑇𝑇𝑅𝑅 𝐷𝐷𝐶𝐶𝐶𝐶𝑇𝑇𝑇𝑇𝐶𝐶𝑐𝑐𝐶𝐶 𝑇𝑇𝐶𝐶𝑇𝑇𝐴𝐴𝐶𝐶𝑅𝑅𝐶𝐶𝑀𝑀[𝐵𝐵𝐶𝐶] × 𝐵𝐵𝐶𝐶𝐶𝐶𝐵𝐵 𝐸𝐸𝐸𝐸𝑇𝑇𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶𝑇𝑇𝐶𝐶𝐶𝐶𝐶𝐶[𝑐𝑐𝑇𝑇𝑇𝑇𝐶𝐶𝑇𝑇𝑇𝑇𝑅𝑅]

𝑇𝑇𝐶𝐶𝑇𝑇𝑇𝑇𝑅𝑅 𝐶𝐶𝑆𝐶𝐶𝑇𝑇𝑇𝑇𝐶𝐶𝐶𝐶𝐸𝐸[𝑐𝑐𝑇𝑇𝑇𝑇𝐶𝐶𝑇𝑇𝑇𝑇𝑅𝑅]

𝐴𝐴𝐴𝐴𝐶𝐶𝐶𝐶𝑇𝑇𝐸𝐸𝐶𝐶 𝐵𝐵𝐶𝐶𝐶𝐶𝐵𝐵 𝑃𝑃𝐶𝐶𝐶𝐶𝑐𝑐𝑆𝑇𝑇𝐶𝐶𝐶𝐶𝐶𝐶[𝐵𝐵𝐶𝐶𝐶𝐶𝐵𝐵𝐶𝐶 𝑃𝑃𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶]

0.0470.12 0.0460.08 0.060.06

0.74

2.9

4

0.9

3

4.1

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

Urban Suburban Rural

Energy per book (MJ)

Bus Train Auto Total

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Energy Use in Shipping and Courier Services

• Total energy used in transporting books from the publisher to the bookstore/ consumer’s home

• Geographical areas taken into account

• Relative population density → efficiency of distribution

•

Distance from centers

• Traditional retail uses trucking firms, E-commerce companies uses courier services.

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Shipping Energy Use per Book

𝑉𝑉𝐶𝐶𝑆𝐶𝐶𝑐𝑐𝑅𝑅𝐶𝐶 𝐹𝐹𝐶𝐶𝐶𝐶𝑅𝑅 𝐸𝐸𝐶𝐶𝐶𝐶𝐶𝐶𝐸𝐸𝐸𝐸 𝑇𝑇𝐶𝐶𝐶𝐶 𝑏𝑏𝐶𝐶𝐶𝐶𝐵𝐵 𝑀𝑀𝑀𝑀 =

�

𝑙𝑙𝑚𝑚𝑙𝑙𝑚𝑚

𝐷𝐷𝐶𝐶𝐶𝐶𝑇𝑇𝑇𝑇𝐶𝐶𝑐𝑐𝐶𝐶 𝐵𝐵𝐶𝐶 × 𝑇𝑇𝐶𝐶𝐶𝐶𝑐𝑐𝐵𝐵 𝐹𝐹𝐶𝐶𝐶𝐶𝑅𝑅 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑇𝑇𝑇𝑇𝐶𝐶𝐶𝐶𝐶𝐶 𝐿𝐿

𝐵𝐵𝐶𝐶 × 𝐹𝐹𝐶𝐶𝐶𝐶𝑅𝑅 𝐸𝐸𝐶𝐶𝐶𝐶𝐶𝐶𝐸𝐸𝐸𝐸 𝐶𝐶𝐶𝐶𝐶𝐶𝑇𝑇𝐶𝐶𝐶𝐶𝑇𝑇 𝑀𝑀𝑀𝑀

𝐿𝐿 × 𝑉𝑉𝐶𝐶𝑅𝑅𝐶𝐶𝐶𝐶𝐶𝐶 𝑆𝑆𝑆𝑇𝑇𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶 𝑇𝑇𝐶𝐶𝐶𝐶𝑐𝑐𝐵𝐵 % × 𝐹𝐹𝐶𝐶𝑅𝑅𝑅𝑅𝐶𝐶𝐶𝐶𝐸𝐸 𝐹𝐹𝑇𝑇𝑐𝑐𝑇𝑇𝐶𝐶𝐶𝐶 % × 𝑁𝑁𝐶𝐶.𝑇𝑇𝐶𝐶𝐶𝐶𝑇𝑇𝐶𝐶 𝑇𝑇𝐶𝐶𝐶𝐶 𝐷𝐷𝐶𝐶𝑅𝑅𝐶𝐶𝐴𝐴𝐶𝐶𝐶𝐶𝐸𝐸

0.16

0.63

2.8

0.042

0.34

1.2

0.073

0.36

1.7

0 0.5 1 1.5 2 2.5 3

Urban Suburban Rural

Energy per book (MJ)

E-commerce Traditional Aggregate

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Siikavirta et al. 2003: Comparing home delivery models

• Five delivery models simulated grocery deliveries in an area around Helsinki

•

Home delivery in two-hour time slots between 17:00 and 21:00

•

Home delivery in one-hour time slots between 12:00 and 21:00

•

Home delivery to reception boxes between 8:00 and 18:00

•

Home delivery once a week per customer to reception boxes between 8:00 and 18:00

•

Purchase in traditional supermarket

LCA Distribution network Delivery models Trad vs. E-com

Future possibilities

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Distance per Order and Numbers of Orders per Route

6.9

3.2

1.6

0.9 0.6

1

22

30

54 55

0 10 20 30 40 50 60

0 1 2 3 4 5 6 7 8

Purchase in traditional

supermarket Home delivery in one-hour

time slots Home delivery in two-hour

time slots Home delivery to reception

boxes Home delivery once a week per customer to reception

boxes

Km/order

km/order Average number of orders per route

Average number of orders per route

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Production and market rebound

• In the case of groceries, a pull-production is feasible

• Lower the risk of overproduction

• Does not apply on all product types

• Morganti et al. 2013: Online interaction allows vendors to inform the customers of environmental and social impacts

• Push the market towards more sustainable demand

• Morganti et al. 2013: demand is increasing for dedicated delivery services and broad product scope

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Rebound effects of the shift from trad.

Retail to E-commerce

• Santarius 2017: Highly accessible internet leads to market transparency → efficient market

• Santarius 2017: Time rebound – saving the extra time and effort from going to store

• Hiselius 2015: Transport behavior for online shoppers in Sweden

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Which alternative is the most environment friendly?

• Sivaraman et al. 2007: E-commerce

system had 0.53-0.62 times the impact of traditional retail.

• Williams and Tagami 2003: Energy

consumption is higher in E-commerce regardless of geographical situation

• Siikavirta et al. 2003: E-commerce is more environment friendly

It’s not crystal clear

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Emission drivers

• Sivaraman et al. 2007

Mode of personal transportation

• Williams and Tagami 2003 Packaging

• Rotem-Mindali and Weltevreden 2013

Product differentiation in courier services

0 1 2 3 4 5 6 7 8

Online Trad Online Trad Online Trad

Energy per book(MJ)

Williams and Tagami 2003:

Total Energy for Sales and Distribution per Book

Packaging Shipping Home/Bookstore energy Consumer travel Rural

Urban

Suburban

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Sensitivity analysis

• Geographic limitation

• Returnings and reshipments unaccounted

• Williams and Tagami 2003: In Japan and U.S. 30%-50% of all books in stores remain unsold

• In clothing and other B2C industries

• Demand development

• Websites designed based on customer behavior to maximize consumption

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Future possibilities

• Packaging material innovation

• Sunstein, Cass 2013: Online nudging for sustainable consumption

• Saberi et al. 2019: Blockchain technology and its relationships to sustainable supply chain

management

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References

1) J, Clement; Statista. 2019. https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/

2) Siikavirta, Hanne; Punakivi, Mikko; Kärkkäinen, Mikko; Linnanen, Lassi. 2003. Effects of E-Commerce on

Greenhouse Gas Emissions – A Case Study of Grocery Home Delivery in Finland. Journal of Industrial Ecology. Vol 6, nr 2.

3) Sivaraman, Deepak. Pacca, Sergio. Mueller, Kimberly. Lin, Jessica. 2007. Comparative Energy, Environmental, and Economic Analysis of Traditional and E-commerce DVD Rental Networks. Journal of Industrial Ecology. Vol 11, nr 3.

4) Williams, Eric. Tagami, Takashi. 2003. Energy Use in Sales and Distribution via E-Commerce and Conventional Retail. Journal of Industrial Ecology. Vol 6, nr 2.

5) Morganti, Eleonora. Dablanc, Laetitia. Fortin, François. 2013. Final deliveries for online shopping: The deployment of pickup point networks in urban and suburban areas. Research in Transport Business & Management. Vol 11, pp 23- 31.

6) Rotem-Mindali, Orit. Weltevreden , Jesse. 2013. Transport effects of e-commerce: what can be learned after years of research? Transportation. Vol 40, pp 867-885.

7) Saberi,Sara. Kouhizadeh, Mahtab. Sarkis, Joseph. Shen, Lejia. 2019. Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research. Vol 57, Issue 7.

8) Tilman Santarius. 2017. Digitalization, Efficiency and the Rebound Effect. Degrowth.info. 16 Februari 2017.

Available at https://www.degrowth.info/en/2017/02/digitalization-efficiency-and-the-rebound-effect/ (Downloaded: 28 October 2019)