Abstract

Product Service Systems (PSS) have received increasing attention as a means for realizing a circular economy. Although PSS are expected to help mitigate the causes of climate change and slow the consumption of scarce natural resources, there is a risk of unintended consequences, resulting in a net increase in environmental impact. Despite this possibility, no effective evaluation method for PSS has been devised. In this study, consequential and attributional Life Cycle Assessment (LCA) approaches, combined with a consumer survey, were conducted to quantify the environmental consequences of PSS using a case study of digital camera rental services in Japan. Attributional LCA showed that greenhouse gas (GHG) emissions in the rental scenario would actually increase relative to the emissions in the conventional purchase-only scenario, from 5.22 kg-CO2 eq. to 18.8 kg-CO2 eq. per individual's use of a digital camera for one year in the case of Digital single-lens reflex camera (DSLR) due to package production and delivery transportation. At the same time, rental services showed the possibility of reducing abiotic depletion potential (ADP) compared to conventional purchase, from 0.667 g Sb-eq. to 0.622 g Sb-eq. in the case of DSLR. The survey revealed four consumer segments emerging after the introduction of PSS: camera owners, hybrid users, shifting rental users, and new rental users, each with different environmental implications. Through consequential LCA, the environmental impacts on a countrywide scale were estimated. For example, GHG emissions were projected to increase by approximately 150 %, mainly caused by delivery processes following the broad acceptance of the camera rental services, whereas ADP increased only slightly due to the comparatively small number of devices needed by these services. The approach used in this study, combining consequential LCA with a consumer survey, points to the need for a better service design that addresses the potential of unintended consequences caused by the introduction of PSS well in advance of any large-scale implementation.

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