The environmental benefits of the circular economy (CE) are often taken for granted. There are, however, reasons to believe that rebound effects may counteract such benefits by increasing overall consumption or ‘growing the pie’. In this study, we focus on two main rebound mechanisms: (1) imperfect substitution between ‘re-circulated’ (recycled, reused, etc.) and new products and (2) re-spending due to economic savings. We use the case study of smartphone reuse in the US to quantify, for the first time, rebound effects from reuse. Using a combination of life cycle assessment, sales statistics, consumer surveying, consumer demand modelling, and environmentally-extended input-output analysis, we quantify the magnitude of this rebound effect for life-cycle greenhouse gas emissions. We find a rebound effect of 29% on average, with a range of 27% to 46% for specific smartphone models. Moreover, when exploring how rebound might play out in other regions and under different consumer behavior patterns, we find that rebound effects could be higher than 100% (backfire effect). In other words, we estimate that about one third, and potentially the entirety, of emission savings resulting from smartphone reuse could be lost due to the rebound effect. Our results thus suggest that there are grounds to challenge the premise that CE strategies, and reuse in particular, always reduce environmental burdens.
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Figure 1. Life cycle greenhouse gas (GHG) emissions (in CO2e, left axis) and environmental rebound effect (in %, right axis) for smartphone reuse, by iPhone model and substitution scenario (perfect vs. imperfect). Perfect substitution scenario assumes 1:1 substitution ratio between used and new phones of identical model and make. Imperfect substitution assumes a mix of alternative substitutes (including new and used smartphones as well as avoided purchases) weighted by our survey results. ‘Reuse’ emissions are those associated with reusing a smartphone, whereas ‘avoided’ emissions are those associated with its alternative(s). GHG emissions are broken down according to the production, transport, recycling, and use phases. Re-spending corresponds to emissions resulting from the economic savings of purchasing a used smartphone instead of its corresponding alternative(s). The ‘average’ model represents a weighted average of all four iPhone models by their eBay sales volume. The rebound effect is expressed as the % of GHG emission savings that are offset due to re-spending.
Figure 2. Environmental rebound effect (ERE) magnitude of reusing a smartphone for selected iPhone models and economies with imperfect substitution. The ‘average’ model corresponds to a weighted average of all four iPhone models by their eBay sales volume. The ERE is expressed as the % of avoided GHG emission that are offset due to re-spending.
Environmental Impacts of Bottled Water vs. Water Fountains
Makov, Tamar, Grégoire Meylan, Jon T. Powell, and Alon Shepon, (2016) "Better Than Bottled Water?—Energy and Climate Change Impacts of on-the-Go Drinking Water Stations," Resources, Conservation and Recycling.
Growing consumption of single-use bottled water has received criticism due to potentially adverse environmental outcomes. Networks of public-sphere water delivery stations have been proposed as a sustainable alternative for water consumption on-the-go, yet the life-cycle impacts of such stations are poorly understood. Here we evaluate the potential cumulative energy demand and climate change impacts of water delivered from a filtered water refill station under various consumption scenarios and provide a comparison to published results for bottled water. Using a hybrid life-cycle analysis framework employing physical and economic data, we model the water station’s performance in four locations: Tel-Aviv, Israel; Miami Beach, Florida, USA; London, UK; and Shanghai, China. We find that the climate change impact of the station is two to six times lower than those of bottled water and that use phase electricity is the most influential factor in determining the station’s environmental impact. We provide additional observations related to scaling up such a system and recommendations to realize further gains in eco-efficiency.
Fig. 2. Woosh station per liter CED and GHG emissions as a function of water volume consumed daily for cooled (panels a,c) and room temperature (panels b,d) scenarios. In the CED cases (panels a,b) the colored layers represent each component’s contribution to overall energy demand in ascending order. In the GHG cases (panels c,d) the gray area represents GHG emissions range when connecting to various electricity grids with different GHG efficiency levels (from top tp bottom) – Chinese grid (red), Israeli grid (orange), average US grid (blue) and average EU grid (green). Bottled water’s average GHG emissions per L are shown in gray (harmonized value for non-cooled bottles transported 100 km) in panel d. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)