Go to the mall or shop on-line—a nagging question for those who want to minimize their environmental footprint. And, of course, now with the social distancing recommended to combat the spread of the COVID-19 virus, it is even more pertinent to know the impact that on-line shopping brings to the environment and to climate change.
A new study by Sadegh Shahmohammadi and colleagues from Radboud University in the Netherlands analyzed three scenarios for shoppers in the United Kingdom that offers some insight to this issue. They compared: 1) traditional shopping in a “brick-and-mortar” store; 2) on-line shopping with home delivery from a local store; and 3) pure-play e-commerce with delivery from a central warehouse (e.g., Amazon). Of course, there are other variants available, but this comparison is instructive. They included storage, packaging and transport of consumer products. They did not consider how far one might drive to the brick-and-mortar store, how many other stops might be made in a single trip, and in what kind of automobile.
In the head-to-head comparison, # 3 (e-commerce from a central warehouse) had greater environmental impact than # 1 (going to the mall). The best was #2, on-line shopping with local delivery, which had 2 to 5 times lower impact than e-commerce from a central warehouse. Of course, going to the mall offers the advantage of comparison shopping, including trying on garments for size, which might otherwise entail returns and exchange of goods via e-commerce. (Each with additional environmental impacts).
To my knowledge, this is the first life-cycle comparison of shopping methods. It highlights the negative impact of e-commerce as it has been intensified by the current attempt to reduce our movements outside of the house to slow the spread of COVID-19. In these turbulent times, it seems best to support local businesses that offer on-line orders with local delivery to your home.
Shahmohammadi, S., and six others. 2020. Comparative greenhouse gas footprinting of online vs. traditional shopping for fast-moving consumer goods: a stochastic approach. Environmental Science and Technology 54: 3499-3509