Measuring CO2 emissions induced by online and brick-and-mortar retailing
2014 (English)Report (Other academic)
We develop a method for empirically measuring the difference in carbon footprint between traditional and online retailing (“e-tailing”) from entry point to a geographical area to consumer residence. The method only requires data on the locations of brick-and-mortar stores, online delivery points, and residences of the region’s population, and on the goods transportation networks in the studied region. Such data are readily available in most countries, so the method is not country or region specific. The method has been evaluated using data from the Dalecarlia region in Sweden, and is shown to be robust to all assumptions made. In our empirical example, the results indicate that the average distance from consumer residence to a brick-and-mortar retailer is 48.54 km in the studied region, while the average distance to an online delivery point is 6.7 km. The results also indicate that e-tailing increases the average distance traveled from the regional entry point to the delivery point from 47.15 km for a brick-and-mortar store to 122.75 km for the online delivery points. However, as professional carriers transport the products in bulk to stores or online delivery points, which is more efficient than consumers’ transporting the products to their residences, the results indicate that consumers switching from traditional to e-tailing on average reduce their CO2 footprints by 84% when buying standard consumer electronics products.
Place, publisher, year, edition, pages
Borlänge: Högskolan Dalarna, 2014.
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2014:14
E-tailing; Spatial distribution of firms and consumers; p-median model; Emission measurement; Emission reduction
Economic Geography Economics Computer and Information Science
Research subject Complex Systems – Microdata Analysis, General Microdata Analysis - methods; Complex Systems – Microdata Analysis, General Microdata Analysis - retail; Complex Systems – Microdata Analysis, General Microdata Analysis - transports
IdentifiersURN: urn:nbn:se:du-15959OAI: oai:DiVA.org:du-15959DiVA: diva2:750036