Sharing economy rebound: The case of peer‐to‐peer sharing of food waste

Abstract

The digital sharing economy is commonly thought to promote sustainable consumption and improve material efficiency through better utilization of existing product stocks. However, the cost savings and convenience of using digital sharing platforms can ultimately stimulate additional demand for products and services. As a result, some or even all of the expected environmental benefits attributed to sharing could be offset, a phenomenon known as the rebound effect. Relying on a unique dataset covering over 750,000 food items shared in the United Kingdom through a free peer‐to‐peer food‐sharing platform, we use econometric modeling, geo‐spatial network analysis, and environmentally extended input–output analysis to quantify how much of the expected environmental benefits attributed to sharing are offset via rebound effects under seven re‐spending scenarios. We find that rebound effects can offset 59–94% of expected greenhouse gas (GHG) emission reduction, 20–81% of expected water depletion benefits, and 23–90% of land use benefit as platform users re‐spent the money saved from food sharing on other goods and services. Our results demonstrate that rebound effects could limit the potential to achieve meaningful reductions in environmental burdens through sharing, and highlight the importance of incorporating rebound effects in environmental assessments of the digital sharing economy.

Full text

DOI: 10.1111/jiec.13319
RESEARCH ARTICLE
Sharing economy rebound
The case of peer-to-peer sharing of food waste
Tamar Meshulam1,2David Font-Vivanco3Vere d Blass2Tamar Makov1
1Faculty of Business & Management, Ben
Gurion University of the Negev, Beer Sheva,
Israel
2The Porter School of the Environment and
Earth Sciences, TelAviv University, Tel Aviv,
Israel
32.-0 LCA Consultants, Aalborg, Denmark
Correspondence
Tamar Meshulam and Tamar Makov, Faculty of
Business & Management, Ben Gurion
University of the Negev, Beer Sheva,Israel.
Email: mtamar@post.bgu.ac.il;
makov@bgu.ac.il
Editor Managing Review: Gang Liu
Funding information
Israel Science Foundation (grant no. 1063/21)
Abstract
The digital sharing economy is commonly thought to promote sustainable consumption
and improve material efficiency through better utilization of existing product stocks.
However, the cost savings and convenience of using digital sharing platforms can ulti-
mately stimulate additional demand for products and services. As a result, some or
even all of the expected environmental benefits attributed to sharing could be off-
set, a phenomenon known as the rebound effect. Relying on a unique dataset covering
over 750,000 food items shared in the United Kingdom through a free peer-to-peer
food-sharing platform, we use econometric modeling, geo-spatial network analysis,
and environmentally extended input–output analysis to quantify how much of the
expected environmental benefits attributed to sharing are offset via rebound effects
under seven re-spending scenarios. We find that rebound effects can offset 59–94% of
expected greenhouse gas (GHG) emission reduction, 20–81% of expectedwater deple-
tion benefits, and 23–90% of land use benefit as platform users re-spent the money
saved from food sharing on other goods and services. Our results demonstrate that
rebound effects could limit the potential to achieve meaningful reductions in envi-
ronmental burdens through sharing, and highlight the importance of incorporating
rebound effects in environmental assessments of the digital sharing economy.
KEYWORDS
environmental input–output analysis, food waste, industrial ecology, multi-regional input–output
analysis, rebound effect, sharing economy
1INTRODUCTION
1.1 Digital sharing economy
Many have argued that the digital sharing economy may herald a revolution, or at least a major disruption, for traditional consumption and pro-
duction systems (Botsman & Rogers, 2011; Heinrichs, 2013). Growing from $15 billion in 2014 to $335 billion in 2025 (Statista, 2020), the digital
sharing economy, as implemented via decentralized, peer-to-peer (P2P) networks of mobile apps, allows people to share and gain access to vari-
ous goods and services (Botsman & Rogers, 2011; Frenken & Schor, 2017; Hamari et al., 2016). While sharing is by no means a new phenomenon
and frequently occurs without digital mediation (Ala-Mantila et al., 2016), the growing popularity of smartphones and internet communications
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial- NoDerivs License, which permits use and distribution in any
medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
© 2022 The Authors. Journal of Industrial Ecology published by Wiley Periodicals LLC on behalf of the International Society for Industrial Ecology.
882 wileyonlinelibrary.com/journal/jiec Journal of Industrial Ecology 2023;27:882–895.

MESHULAM ET AL.883
technologies have lowered transaction costs, and allowed sharing networks to expand far beyond circles of immediate family or friends, opening up
opportunities for sharing between strangers (Mair & Reischauer, 2017; Richards & Hamilton, 2018; Schor, 2014,2020).
Beyond its social and economic implications, the digital sharing economy (referred to hereafter as sharing or sharing economy in short) is
commonly thought to deliver environmental benefits through more efficient use of existing product stocks ranging from vacant apartments, to
unoccupied car seats, to edible yet unwanted food (Botsman & Rogers, 2011; Makov et al., 2020; Nijland & van Meerkerk, 2017). Yet despite its
apparent contribution to material efficiency, the overall impact of the sharing economy and whether it reduces environmental burdens in practice
are not yet well understood and the literature reports conflicting results.
Some suggest that sharing is better from an environmental standpoint compared to traditional consumption patterns. For example, Chen and
Kockelman (2016) find that car sharing could reduce users’ greenhouse gas (GHG) emissions by up to 51%; Wasserbaur et al. (2020) suggest that
sharing can lower emissions associated with laundry by 30%; Kerdlap et al. (2021) reveal that stroller rentals can reduce environmental impacts by
29–46%; and Martin et al. (2019) estimate that P2P ski rentals can reduce carbon emissions by as much as 80%.
Critically,however, these studies often do not consider the varied ways in which sharing can affect consumer behavior more broadly. For example,
the convenience and cost savings offered by many sharing economy models can trigger additional demand and spending and therefore lead to an
overall increase in the number of units consumed compared to the baseline in which sharing did not exist. Research reveals that under certain
circumstances, sharing can boost demand for durable goods including new cars, bicycles, and housing (Gong et al., 2017; Horn & Merante, 2017;
Ma et al., 2018; Tussyadiah & Pesonen, 2015), as well as increase demand for services such as tourism and transport (Diao et al., 2021;Gur
˘
au &
Ranchhod, 2020; Tussyadiah& Pesonen, 2018). In addition, sharing does not always displace the products and services it is expected to displace. For
instance, several studies show that car sharing displaces not only single passenger rides, but also more sustainable transport modes such as public
transport and walking (Clewlow & Mishra, 2017; Diao et al., 2021; Gehrke et al., 2019; Jung & Koo, 2018; Lee et al., 2019). For a more extensive
review of empirically driven assessments of sharing economy environmental performance, see Meshulam et al.’s (2022) working paper.
Ultimately, while sharing might be more environmentally efficient per unit consumed, it may lead to an overall increase in the total number of
units consumed. As a result, some or even all of the expected environmental benefits could be offset—a phenomenon typically studied under the
“rebound effect” framework (Acquier et al., 2017; Galvin, 2020; Font-Vivanco & Makov, 2020; Makov & Font-Vivanco, 2018). For example, Amatuni
et al. (2020) reveal that rebound could offset 5–55% of the expected benefits of car sharing in part because users switch from public transport to
car sharing. Cheng et al. (2020) suggest that emissions associated with sharing via Airbnb are five to seven times higher when including the impacts
associated with host earnings. Similarly,Warmington-Lundström and Laurenti (2020) find that the 47% of carbon benefits attributed to boat sharing
are eroded as lessors re-spend the money they earned by renting their boats.
Despite evidence that wide-scale adoption of digitally facilitated sharing can increase overall consumption, rebound effects are mostly absent
from sharing economy sustainability assessments (Henry et al., 2021). As such, the environmental benefits associated with the sharing economy
and its potential to address sustainability challenges such as climate change or food waste might be systematically overestimated.
We aim to fill these knowledge gaps through a data-driven analysis of OLIO—a UK-based startup which operates a P2P, location-based, food-
sharing platform with over four million registered users worldwide (https://olioex.com/). The platform helps reduce food waste by matching users
with edible yet unwanted food to other users who are interested in collecting and consuming that food. While OLIO is a for profit company, all
foods, including those donated by local shops, delis, and bakeries are shared by users free of charge. In other words, OLIO acts as a free digital
marketplace for “second hand” food. Focusing on the United Kingdom—the largest and most developed food-sharing network, we use the platform
as an illustrative case study to estimate rebound effects of zero cost digital sharing economy platforms. We employ a combination of data-science
methods, econometrics, and environmentally extended input–output analysis (EEIO) to quantify how much of the expected environmental benefits
attributed to sharing are potentially offset through rebound effects. Specifically, we quantify rebound related to re-spending, namely, the environ-
mental impacts incurred as collecting users re-spend the money they saved by sourcing free food through the platform and since we do not have
specific data on users’ re-spending patterns, we model rebound under seven different re-spending scenarios. The following sections of the intro-
duction present a brief overview of relevant literature on food waste and rebound effects. Next, we present a detailed description of our unique
dataset and methodological approach (Section 2), as well as our results (Section 3). Finally, we discuss the limitations of this study as well as the
broader implications of our findings in the discussion (Section 4).
1.2 Food waste
Globally, roughly 2.5 billion tons of food produced for human consumption is lost along the supply chain each year(WWF-UK, 2021). The production
of food which ends up as waste is associated with 20% of the freshwater consumed, 30% of global agricultural land use, and 8% of GHG emissions
(FAO, 2013, 2018). Wasted food includes both food loss, a term typically used to describe losses which occur during the production stages (specifi-
cally at the field, harvest, or post-harvest stages), and food waste, which refers to losses occurring at the retail and consumer end of the supply chain
(Parfitt et al., 2010).

884 MESHULAM ET AL.
In high-income countries, food waste at the retail and household level is pervasive (United Nations Environment Programme,2021). In the United
States, for example, annual per capita food waste generation was roughly 120 kg, adding up to over 63 million tons of food waste per year, while
in the United Kingdom, per capita generation of food waste is a more modest yet still astonishingly high 94 kg per capita, which comes out to 9.5
million tons per year (U.S. Environmental Protection Agency, 2020;WRAP,2020).
The staggering amounts of food waste have led to public outcry and sparked interest in digitally mediated secondary marketsfor food redistribu-
tion and sharing among peers as potential strategies to combat food waste (Ciccullo et al. 2022;Davies&Evans,2019; Galbraith, 2012; Harvey et al.,
2020;Neslen,2016; Richards & Hamilton, 2018; UNEP DTU Partnership & United Nations Environment Programme, 2021). For example, Richard
and Hamilton (2018) demonstrate that digital platforms operating as two-sided markets can increase uptake of surplus foods (e.g., ugly produce).
Manshadi and Rodilitz (2022) examine food redistribution networks and propose a model to optimize participation and engagement of food res-
cue volunteers. Focusing on P2P sharing, Mazzucchelli et al. (2021) identify familiarity with food sharing and environmental and social consumer
perception as key factors in a food-sharing platform’s potential success. Harvey et al. (2020) examine users’ roles within food-sharing networks and
whether they evolve over time while Makov et al. (2020) quantify the GHG emission reduction associated with P2P food sharing and demonstrate
that sharing has environmental benefits. Finally, Gallo (2021) surveys users of food redistribution and sharing platforms and identifies economic
savings, followed by environmental, ethical, and social considerations as the central motivating factors for participation.
Thus, while past work suggests that digital food sharing can deliver environmental benefits (Makov et al., 2020), most studies to date stop short
of examining whether food sharing triggers rebound effects as consumers re-spend the money they saved collecting free food. If, for example,
consumers re-spend money saved via sharing on more carbon-intensive products and services (e.g., flights), sharing might not lead to the expected
environmental benefit.
1.3 A brief introduction to the rebound effect
The rebound effect is a construct used to describe a variety of consumer and market responses to improved efficiency, which influence demand
and can lead to an overall increase in consumption relative to a baseline in which these responses do not occur (Acquier et al., 2017; Galvin, 2020;
Makov & Font-Vivanco, 2018; Font-Vivanco & Makov, 2020). The basic idea of the rebound mechanism suggests that improving efficiency effectively
reduces the unit usage price. The lower price then spurs demand, leading to more consumption overall compared to a counterfactual baseline in
which there was no improvement in efficiency.
Research on rebound effects originates from the work of the economist William Stanley Jevons, who argued that efficiency gains in the use of coal
would eventually cause an increase in the total demand for coal (Jevons, 1865). In the 1980s, following the energy crisis, energy economics adopted
and enhanced Jevon’s argument (Font-Vivanco et al., 2016). Leonard Brookes (1979) and Daniel Khazzoom (1980) proposed and formalized the
rebound effect, from a macroeconomic and microeconomic perspective, respectively. Well-documented examples of the rebound effect in energy
economics include: increased energy demand following household energy efficiency improvements, longer distances driven in response to more
fuel-efficient vehicles and cheaper operating costs, and lights left on longer after installation of energy-efficient light bulbs (Chitnis et al., 2014;
Greening et al., 2000; Schleich et al., 2014). In some cases, efficiency gains and the added consumption they induce can result in a net increase in the
amount of resources used and emissions generated. These extreme cases are commonly known as backfire rebound effects (Sorrell, 2007).
While earlier work on rebound effect centered mostly on energy, more recently researchers have expanded the concept’s scope to include
a multitude of environmental impacts (Freire-González & Font-Vivanco, 2017; Thiesen et al., 2008), and various non energy-related products,
including construction materials, dietary changes, consumer electronics, food waste, and more (Bahn-Walkowiak et al., 2012; Chitnis et al., 2013,
2014; Druckman et al., 2011;Grabs,2015; Hagedorn & Wilts, 2019; Makov & Font-Vivanco, 2018; Martinez-Sanchez et al., 2016;Murray,2013;
Salemdeeb et al., 2017). Building on this work, here we examine what has been termed the “Environmental Rebound effect” (Hertwich, 2005;
Font-Vivanco et al., 2016).
Traditionally, the literature distinguishes between three types of economic mechanisms leading to the rebound effect: direct rebound effects,
indirect rebound effects (both at the microeconomic level), and economy-wide rebound effects (at the macroeconomic level). Direct rebound is
used to describe increased demand for a particular product or service following efficiency improvements (e.g., new business model, lean production)
in the provision of that particular product. In microeconomic terms, this mechanism is a combination of price and substitution effects. Indirect
rebound effect refers to the increased consumption of other goods and services as a result of more available free income due to the increased
resource efficiency of the targeted product. In this case, reduction in the price of a product leads to residual savings, which are used on other goods
or services. This is also referred to as the “income” or “re-spending” effect. Finally, the sum of both micro- and macroeconomic rebound effects over
an economy is labeled as the economy-wide rebound effect and leads to large-scale readjustments to economic structure and final demand across
multiple sectors throughout the entire economy (Greening et al., 2000; Sorrell, 2007).
Sharing platforms are highly efficient at matching access supply with unmet demand (Botsman & Rogers, 2011). In the context of food, digital
sharing can improve material efficiency and reduce food waste by lowering transaction costs, expanding networks of supply and demand, and pro-
viding a pathway to reuse rapidly perishable items such as fresh produce, which have few secondary outlets once they reach the household level

MESHULAM ET AL.885
FIGURE 1 Conceptual diagram of methodology. From left to right—Data preparation: assign users to income deciles, classify listings to food
categories; Retail value estimation: sample retail value and estimate retail value using Monte Carlo (MC) simulations; Assessment of
environmental benefits associated with food waste reductions using environmentally extended input–output (EEIO). Assessment of added
environmental impact of re-spending calculated using Almost Ideal Demand Model (AIDS) and EEIO models
(Falcone & Imbert, 2017; Makov et al., 2020; Michelini et al., 2020; Richards & Hamilton, 2018). Such efficiency gains lead to lower costs and thus
savings for consumers who source food via sharing platforms. As a result, digital food sharing could potentially trigger rebound effects as consumers
re-spend the money saved on purchasing food via conventional channels.
2METHODS AND DATA
To examine rebound effects of P2P food sharing, we rely on data provided by OLIO—a free, P2P sharing platform and adopt a multi-methods
approach drawing from economics, industrial ecology, and data-science. Focusing on the United Kingdom, we began by estimating the retail value
of all food items shared through the platform in the United Kingdom and then used EEIO to quantify the environmental benefits of sharing in terms
of global warming, water depletion, and land use. We specifically chose to focus on these impact categories as they are the ones most relevant to
agriculture and food production (FAO, 2013, 2018), and allow comparison to past work on rebound effects.
Next, we constructed several scenarios to reflect different re-spending patterns using the Almost Ideal Demand System (AIDS) model (Deaton &
Muellbauer, 1980; Vanham et al., 2015; Font-Vivanco & Makov, 2020 ;WRAP,2013). In line with previous work, most of our scenarios assume that
collecting users re-spend all the marginal savings they gain from sourcing free food. In addition, we include two scenarios, which reflect downshifting
and placing some of the money in savings (see Section 2.4.1 and Table 1). We then used EEIO to quantify the environmental impacts associated with
the added consumption under each re-spending scenario. Finally, we examined the share of environmental benefits offset as a result of re-spending
to assess the magnitude of the rebound effect. Figure 1presents a graphical overview of the methodology and data treatment processes used and
the following sections of the methodology present a detailed account of our empirical approach.
We chose to model environmental impacts using EEIO for several reasons. First, EEIO has been extensively used to quantify the environmental
impacts associated with household consumption patterns and lifestyles (Bjelle et al., 2021; Ivanova et al., 2016; Minx et al., 2009). Second, EEIO
is particularly well suited for research into re-spending rebound effects as its basic unit of analysis is monetary and not physical (i.e., mass which
is typically used in process-based life cycle assessment). Finally, using EEIO allows us to employ a consistent methodological approach to quantify
both the benefits of sharing and the added consumption (and subsequently rebound effect). As such, using EEIO strengthens the internal validity of
our approach and results.
2.1 Data preparation
Raw data provided by OLIO included detailed information on roughly 860,000 listings posted to the platform worldwide between April 2017 and
February 2020. For each listing, the data included a unique listing ID, title, description, collection notes, exact geolocation where the item should
be picked up, the unique ID of the user offering the item, a timestamp indicating when the listing was posted, and an indication whether the item
was successfully shared or remained unclaimed. For collected (i.e., shared) listings (60% of all listings posted), the raw data also included a unique
collection ID (or IDs when the listing offered multiple food items), and a timestamp logging the time the collection was marked as completed. Finally,

886 MESHULAM ET AL.
the data also included users’ default notification geo-locations, generally assumed to be their home address, and whether they acted as official
platform volunteers who salvage food from local cafes, bakeries, and supermarkets for redistribution via the platform.
Focusing on the United Kingdom—the largest and most developed of the sharing networks—we first classified the 380,000 listings that
were successfully shared into 13 food categories (see supporting information SI-1) using a supervised deep learning long short-term mem-
ory (LSTM) network classifier, developed originally for Makov et al., 2020 (for more details and specifications, see SI and methods section in
Makov et al., 2020).
Next, to understand which income deciles participate in food sharing and model re-spending of money saved, we assign each user to their respec-
tive income deciles. We first mapped each user to a specific lower layer super output area (LSOA) based on their default notification geo-location.
LSOA is a small geographical area with a mean population of 1500, developed bythe UK office for National Statistics to improve small area statistics
reporting. Next, each user was assigned to the income decile of their LSOA according to the 2019 UK Index of Multiple Deprivation. We used the
Geopandas and Shapely Python packages together with official shapefiles published by the UK Office for National Statistics and the UK 2019 Index
of Multiple Deprivation (Gillies et al., 2007; Jordahl, 2014; Ministry of Housing, Communities & Local Government, 2019).
Listings belonging to food categories with fewer than 750 successful exchanges, or listings for which we could not map both users to their income
deciles were excluded leaving roughly 12 food categories and 365,000 listings for analysis.
2.2 Retail value estimation
To estimate the retail value of all foods successfully shared via the platform, we followed the following procedure:
1. Randomly selected 3,100 items out of all collected listings, ensuring that we have at least 60 samples per each of the 12 food categories.
2. Manually assigned a retail value according to each listing’s text description, notes, and images as posted to the platform to generate what we
refer to as the retail value sample (see Figure 1).
3. Examined retail values within each food category in our retail value sample and found that they are not normally distributed. In addition, we
revealed differences in retail value distributions between listings posted by regular users and those posted by food waste heroes (official plat-
form volunteers) as they were more likely to offer multiple items in each listing. Therefore, we split the data into 24 subgroups according to the
12 food categories and 2 user types.
4. We then fitted each of the 24 user-food category subgroups with 80 different distribution functions using Virtanen et al. (2020) SciPy python
package, and found that the lognormal probability distribution function best reflected the distributions across all subgroups.
5. As retail prices were not normally distributed, we could not derive overall values based on means. Therefore, we used a series of Monte Carlo
(MC) simulations where we sampled the fitted distributions (with repetition) ntimes, where nis the total number of shared listings in each
subgroup, and summed the retail value of all listings per subgroup and overall. We repeated this procedure 10,000 times to derive the average
total retail value of shared foods and its 5th and 95th percentiles (See Table SI-4 in the supporting information for detail on MC simulation).
In mathematical terms, the Monte Carlo simulations can be described as (adapted from Makov et al., 2020):
RTOT =∑2
u=1∑12
g=1∑n(g,u)
i=1Fgu(i) (1)
where RTOT represents total retail value of all subgroups, uis the user group, gthe food category group, n(g,u) is the number of listings exchanged
per category and user, and Fgu(i) is the random sampling of item ifrom the fitted probability distribution function for that subgroup.
2.3 Environmental benefits of avoided food waste
Building on our interim results for retail value of shared food items, we then estimated the environmental benefits associated with food sharing
using EEIO Analysis with EXIOBASE3 (Stadler et al., 2018).
EEIO analysis is based on the input–output (IO) framework, a macroeconomic tool defined by Nobel Prize winner Leontief in the 1930s (Leontief,
1970; Miller & Blair, 2009). The IO framework uses tables of national sectoral aggregated data with inter-industry relationships to model how
change in household and government demand, termed final demand, will affect these inter-industry relationships. EEIO extends the IO frame-
work by adding environmental stressors at the sector level, which allow researchers to use data in monetary units to model environmental impacts
including global warming, land use, water depletion, and more (Duchin, 1992; Joshi, 1999; Miller & Blair, 2009).

MESHULAM ET AL.887
Here, we use EXIOBASE3, 2019, a monetary multi-regional IO modeling database, which was designed for environmental analysis. EXIOBASE 3
maps 44 countries as well as five “rest of the world” regions (Stadler et al., 2018), and allowed us to account for environmental impacts at both the
local and global level—a critical aspect in the current case since the United Kingdom relies heavily on food imports (de Ruiter et al., 2016; Salemdeeb
et al., 2017).
Specifically, to estimate the environmental benefits associated with food sharing, we first matched each of our OLIO food categories to the most
appropriate EXIOBASE3 sector. When possible, OLIO food categories were matched to their direct analogs, for example, dairy for dairy products
sector. When OLIO categories had no clear EXIOBASE3 analog, they were matched to EXIOBASE3’s residual food sector (see supporting informa-
tion SI-2 for details). We then assigned the retail value of all food shared in each food category to its respective EXIOBASE3 sector, converting to
basic prices using supply and use tables from ONS (2021) (see supporting information SI-6 for details) and from British pound (£) to 2019 Euro (€)
(the base year for EXIOBASE 3) based on the exchange rate of the European Central bank.
For each sector, we then (1) disaggregated the retail value to account for imports according to EXIOBASE3 import coefficients, (2) quantified
environmental benefits according to EXIOBASE3 stressor coefficients using the pymrio Python package (Stadler 2021; Stadler & Didier, 2020),
and (3) converted from EXIOBASE3 stressors (e.g., CO2,CH
4, blue water) to environmental impact indicators (e.g., global warming) following the
approach outlined in Tukker (2016). Specifically, we converted GHG emissions into GWP100, and aggregated all water and land stressors into water
depletion and land use.
2.4 Environmental impacts of re-spending
To quantify the environmental burdens incurred as users re-spend the money they had saved via sharing on additional products and services, we
first constructed several scenarios to model what percent of savings is re-spent and of this amount, how users re-repent it (see Table 1). We then
followed a similar approach to the one described above, matching consumption categories to their respective EXIOBASE3 sections and calculating
environmental impacts using GWP100, water depletion, and land use.
To model how users re-spent the additional marginal income, we used the well-established AIDS consumer demand model developed by Deaton
and Muellbauer (1980). The AIDS consumer demand model uses historical data on household spending (e.g., surveys, national accounts) to estimate
what share of a household’s budget is spent on each specific consumption category (e.g., transport, heating, food). Commonly applied in the study
of rebound, AIDS is used to model how households typically re-spend marginal sums of money saved across consumption categories (Makov &
Font-Vivanco, 2018;Murray,2013; Salemdeeb et al., 2017; Vélez-Henao et al., 2020).
In mathematical terms the AIDS model can be expressed as:
wi
t=𝛼
i+𝛽
iln (xt
Pt)+∑j=1..n𝛾iln(pj
t) (2)
where wis the marginal budget share for the ith consumption category at a given time period t,nis the number of consumption categories,
xt is total expenditures,Pis defined here as the Stone’s price index, pis the price of a given category, and α,β,andγare the unknown
parameters.
Using the AIDS linear approximation R package, we modeled our baseline re-spending scenario based on the 2002–2019 UK household budget
surveys and UK price indices to reflect how UK households typically re-spend marginal savings. Specifically, we allocated user’s marginal savings
across 12 consumption categories defined according to the classification of individual consumption according to purpose (COICOP) (Eurostat,
2021a; Henningsen, 2017; UK Office for National Statistics, 2021).
2.4.1 Re-spending scenarios
Since results are highly susceptible to assumptions on the percent of the marginal income re-spent and to re-spending modeling choices, we con-
structed seven scenarios, presented in detail in Table 1(Cheng et al., 2020; Druckman et al., 2011). Most rebound studies assume that marginal
income due to efficiency gains is fully re-spent. If, however, using OLIO is part of a downshifting lifestyle choice, it is possible that users engage
in food sharing as it allows them to work less and get by on lower earnings. In other words, sourcing free food might allow them to reach the
same available income with fewer working hours (Hanbury et al., 2019; Sorrell et al., 2020). Alternatively, users might choose to not re-spend all
of the money, but direct some of it to their savings. UK households saved 5% of income during 2019 (ONS, 2022). Scenarios 1–3 describe these
possibilities.
Prior research suggested that lower income populations tend to re-spend marginal savings on more carbon-intensive consumption categories
such as transport and housing compared to higher income populations (Chitnis et al., 2014;Grabs,2015;Murray,2013). As 64% of collections

888 MESHULAM ET AL.
TAB L E 1 Re-spending scenarios
Scenario id Scenario Type Description
(1) Baseline Money saved via food sharing is re-spent according to the marginal spending patterns of the
general UK population
(2) Downshifting Percent re-spent Marginal income is used to downshift and reduce work hours
(3) Savings Percent re-spent 5% of the marginal income is saved (based on UK households’ saving ratio), the rest is
re-spent according to the marginal spending patterns of the general UK population
(4) 2nd Income decile Income All collecting users are associated with the 2nd income decile. Money saved via food sharing
is thus re-spent according to the marginal spending patterns (i.e., MBS) of the UK
population in the 2nd income decile (the most common income decile among platform
users).
(5) 10th Income decile Income All collecting users are associated with the 10th (i.e., highest) income decile. Money savedvia
food sharing is thus re-spent according to the marginal spending patterns (i.e., MBS) of the
UK population in the 10th income decile
(6) No re-spending on
food
Food Money saved via food sharing is re-spent according to the marginal spending patterns (i.e.,
MBS) of the general UK population assuming no money is re-spent on food
(7) 50% re-spending on
food
Food Half of the saved expenditure is re-spent on food. The second half is spent by calculating
MBS on 11 (excluding food) consumption categories with the spending pattern of the UK
general population
were made by users associated with lower (rather than higher) income deciles, our baseline scenario might not accurately reflect the way collectors
re-spent marginal savings (see supporting information SI-3 for users’ income distribution). To address income decile variability, we constructed
Scenarios 4 and 5. Additionally, it remains unclear to what degree savings resulting from the adoption of various food waste reduction strategies
are re-spent on additional food purchases. Some argue that abetment of food waste is a conscious decision and thus none of the money saved from
food waste reduction at the household level is re-spent on food (Chitnis et al., 2014; Druckman et al., 2011; Hagedorn & Wilts, 2019). In contrast,
a recent study on food redistribution platforms in Europe found that 17% of platform users choose to re-spend most of the money they saved on
food (Gallo, 2021). Relatedly, findings reported by WRAP suggest that UK consumers re-spend about 50% of savings from food waste reduction
strategies on higher value foods (Salemdeeb et al., 2017;WRAP,2014).
2.5 Environmental rebound effect
Environmental rebound effect (ERE) is defined as the percent of expected environmental benefits which are nullified via impacts associated with
re-spending (Font-Vivanco et al., 2016).
In mathematical terms, ERE can be expressed as (derived from Font-Vivanco et al., 2014):
%ERE =(AEI
|PEB|)∗100 (3)
where, AEI represents added environmental impacts (associated with re-spending) and PEB represents potential environmental benefits
(associated with food sharing)
Building on our results for potential environmental benefits from food sharing and added environmental impacts from re-spending, we used the
equation above to assess the ERE of food sharing for three environmental indicators: global warming, water depletion, and land use. Note that we
do not use confidence intervals in assessing rebound effect, since rebound is a proportion and as such would yield the same results when calculated
for mean and confidence interval values.
3RESULTS
Between April 2017 and February of 2020, OLIO users in the United Kingdom collected nearly 550,000 food items, from 365,000 different listings,
at an estimated retail value of £2.98 million (with £2.9 and £3.07 million as the 5th and 95th percentiles, respectively; see supporting information
SI-4 on retail value of different food categories). The most popular shared foods, namely, baked goods, sandwiches, prepared food, and kitchen and
pantry staples, collectively accounted for 75% of all retail value saved.

MESHULAM ET AL.889
FIGURE 2 Share of benefits offset via rebound effects by impact category and re-spending scenario. (a) Share of expected GHG emissions,
water depletion, and land use benefits offset by rebound under the baseline scenario. (b) Share and absolute values for expected global warming
benefits offset via rebound by scenario. Absolute values are in tons of CO2-eq. (c) Share and absolute values for expected water depletion benefits
offset via rebound by scenario. Absolute values are in thousand cubic meters. (d) Share and absolute values for expected land use benefits offset
via rebound by scenario. Absolute values are in km2.Figure2b–d presents expected benefits (in red) and results for baseline scenario (in gray
dotted line). Underlying data for Figure 2are available in the supporting information tables SI-7 and SI-8
The environmental benefits associated with these collections amount to 1,265 tons of CO2-eq (with a 5th and 95th percentiles interval of [1,231,
1,303]), 96,100 cubic meter of fresh water ([93,700, 98,600]), and 4.1 km2of land ([3.99, 4.23]). However, we find that in most scenarios, a substan-
tial share of these benefits is eroded when accounting for rebound effects, namely the environmental impacts incurred as users re-spend money
saved via sharing on additional products and services. Under the downshifting scenario, the rebound effect equals zero as the money saved via
sharing is assumed to compensate for fewer working hours and a lower income. In other scenarios, we find that rebound effects can offset 59−94%
of expected GHG emission reduction, 20−81% of water depletion, and 23−90% of land use benefits (see Figure 2and supporting information
table SI-8).
Examining differences across environmental impact categories, we find that rebound effects are higher for GHG emissions, compared to water
depletion or land use under all scenarios (see Figure 2). For example, our results suggest that under the baseline scenario (Scenario 1), which reflects
how the average UK household re-spends marginal savings on different consumption categories, 68% of the expected global benefits of P2P food
sharing are eroded via re-spending, compared to only 35% and 40% of expected benefits related to water depletion and land use (respectively).
In line with past research, we find that rebound effects are somewhat larger when users are assumed to belong to lower income deciles
(Scenario 4) compared to higher income deciles (Scenario 5). Specifically, rebound effects offset 70%, 41%, and 46% of the expected GHG, water,
and land use benefits (respectively), when all users are assumed to belong to the second income decile (Scenario 4) compared with 66%, 31%, and
36% of GHG, water, and land use benefits, respectively, when all users are assumed to come from the highest income level (10th decile, Scenario 5).
More notably, however, the share of savings that users spend on food purchases has a substantial impact on rebound effects. Specifically, we find
that if users use half of the money they saved via sharing to buy food (Scenario 7), 81% of the expectedwater depletion benefits of sharing are offset.
This is almost four times higher than the 20% of water depletion benefits offset when users do not re-spend savings on food (Scenario 6), and more
than twice as much compared to 35% of benefits offset under the Baseline scenario. Rebound is even larger when considering global warming and
land use, where 94% and 89% of expected benefits are offset (compared to 59% and 23%, respectively, under Scenario 6).

890 MESHULAM ET AL.
FIGURE 3 Re-spending and relative contribution to rebound effect by consumption category (baseline scenario). Figure 3presents
different consumption categories’ relative contribution to re-spending and rebound under the baseline scenario. Radial axis depicts different
consumption categories. Contribution to total re-spending is marked in red line, contribution to global warming rebound is marked in gray,
contribution to water depletion rebound is marked in green, and contribution to land use rebound in light brown. Underlying data for Figure 3are
available in the supporting information Table SI-9
Repeated analysis where we used data from GDP national accounts Eurostat (2021b) to model marginal re-spending patterns for Scenarios
1, 6, and 7 yielded similar results confirming the robustness of our finding (see supporting information SI-8 and Eurostat (2018) for more on the
distinction between household budget survey and GDP national accounts).
A deeper drill down into the underlining consumption categories (e.g., food, clothing and footwear, recreation) revealed wide variation in their
relative contribution to rebound under the baseline scenario (see Figure 3). This variation is a factor of the relative share of savings re-spent on each
consumption category and each category’s carbon, water, and land intensities (see supporting information SI-5 and SI-9). Food consumption stands
out as one of the most environmentally impactful consumption categories responsible for 24% of global warming rebound, 51% of water depletion
rebound, and 49% of land use rebound. For global warming rebound, other relevant consumption categories are transport, recreation, and housing,
while for water depletion and land use, recreation makes the largest contribution beyond food.
4DISCUSSION
The digital sharing economy is often hailed as a promising path toward sustainable consumption, yet our empirically driven results suggest that
rebound effects can severely limit the potential environmental benefits of sharing and call into question whether sharing can live up to its environ-
mental promise. Using a comprehensive dataset covering all food items shared via OLIO in the United Kingdom between April 2017 and February
of 2020, we show that rebound can offset as much as 20−94% of the expected environmental benefits, depending on the impact category and
re-spending scenario examined.
While we find relatively high rates of rebound effects compared to analyses focusing on energy-related technologies or transport (Gillingham
et al., 2013), our results are well aligned with past work on rebound stemming from food waste abatement strategies (Chitnis et al., 2014; Druckman
et al., 2011; Sorrell et al., 2020). Critically, our findings illustrate that when sharing is adopted as part of larger lifestyle change, such as downshift-
ing, rebound estimates are markedly lower. Our work adds to the emerging body of work on sharing economy rebound effects and highlights the
importance of including rebound as an integral part of environmental assessments of sharing.
In addition, our work makes several theoretical contributions. First, we demonstrate that rebound effects may vary substantially based on the
specific impact categories examined. For instance, we find that under the baseline scenario, 68% of anticipated global warming benefits of food
sharing are offset as users re-spend the money they had saved by collecting free food. When examining rebound in terms of water depletion and
land use, however, we find that only 35% and 40% of benefits associated with food sharing (respectively) are nullified via rebound. These stark
differences emphasize the need to go beyond global warming and also consider manifestations of rebound across other environmental impact
categories, especially ones that pertain to the specific domain examined.
Second, our results confirm that rebound for lower income populations is higher than that of higher income populations (Chitnis et al., 2014;
Grabs, 2015;Murray,2013). This is not surprising as lower income households tend to re-spend marginal additions to their available income on

MESHULAM ET AL.891
relatively carbon-intensive consumption categories (e.g., housing and transport). Nonetheless, our results suggest that while income level has a
relatively modest impact on rebound size, assumptions regarding the share of savings that are re-spent on food lead to substantial differences in
rebound magnitude. While some previous studies assumed that food waste reduction is an intentional practice households adopt and thus any
saving generated would not be re-spent on buying additional food, this might not be relevant in the context of the sharing economy. A recent small-
scale study exploring the re-spending habits of people participating in food sharing indicates that roughly one out of six use money saved from food
sharing to buy food (Gallo, 2021). As assumptions on whether and how households re-spend marginal additions to their income can meaningfully
influence rebound results, there is great need for more granular, domain-specific information on re-spending patterns and the factors that might
affect them.
Finally, given that re-spending is a major driver for sharing economy rebound, it follows that free sharing platforms, which by default can save
users a lot of money, are particularly susceptible to high rebound effects. The recognition that free sharing would ultimately lead to higher rebound
effects calls into question recent work which suggests that business models that minimize economic incentives for participating in P2P sharing tend
to be more environmental than other sharing economy business models (Curtis & Mont, 2020; Laukkanen & Tura, 2020).
Yet while re-spending may erode some of the environmental benefits attributed to sharing, it may very well deliver important economic and
social benefits. For example, food sharing can increase people’s access to healthy nutritious food and help address food insecurity (Davies & Evans,
2019). This is particularly important in the age of the Covid-19 pandemic, which has exacerbated economic inequalities and led to record rates of
food insecurity. In addition, sharing, especially the type which necessitates physical interactions, can strengthen community ties and contributeto
users’ social welfare and well-being. For example, lower income populations could use the additional available income (i.e., savings from collecting
free food) to better meet the basic needs of their households. As such, there is great need for a more holistic evaluation of rebound effects which
weighs not only the added environmental consequences, but also the potential beneficial impacts it may have on social and economic welfare of
different socio-demographic groups.
This research has several important limitations. First, many foods offered for sharing via the platform contained both plant-based as well as
animal-based ingredients, which made it hard to assign them to specific food sectors. While using EEIO allowed us to apply a consistent approach
to model both environmental benefit of food sharing and rebound effects, its aggregated nature precluded a more nuanced distinction between
plant-based and animal-based foods and therefore might create inaccuracies in the environmental benefits estimation. The use of multi methods
can also increase inaccuracies, as each method transforms the data, it can aggregate overallerror. In addition, the embedded assumption that shared
food items displace purchasing of new food items, and subsequently their production at a 1:1 ratio is questionable. It is possible that users would
not have bought the same items they collected. For example, if food sharing was not an option, users could have purchased less food items or
simply cheaper ones than the ones they collected. But even if saving and subsequently re-spending would have been lower, the size of the rebound
effect would have stayed the same since it is expressed in relative terms. Finally, we assume that people using OLIO follow the same homogeneous
expenditure patterns as the general population, or their income declines (dependingon scenario). More work is needed to ascertain whether sharing
economy users, and platform users in particular, use marginal additions to their disposable income differently. For example, one could argue that
people who engage in food sharing are more pro-environmental and thus tend to follow more sustainable consumption patterns. Future research
should complement our findings with in-depth interviews and surveys of food redistribution users to better understand how different factors such
as socio-demographic characteristics and environmental attitudes affect re-spending patterns and subsequently rebound effects.
In sum, the digital sharing economy is often discussed in the context of sustainable lifestyles and the transition toward circular production and
consumption systems, where resource efficiency is maximized and wastes minimized. Yet as this work demonstrates, the cost savings and con-
venience offered by sharing platforms can ultimately lead to additional spending and offset some or even all of the environmental benefits of
sharing. While this by no means suggests that the potential social or economic benefits of sharing are trivial, it questions the premise that mean-
ingful progress toward climate change mitigation or other grand sustainability challenges can be achieved through wide-scale adoption of sharing
economy models.
ACKNOWLEDGMENTS
We thank OLIO for providing the data and Alon Shepon, Stav Rosenzweig, Ofir Rubin, and the PLATE community for their thoughtful feedback and
suggestions. This work was supported by the Israel Science Foundation (grant no.1063/21).
CONFLICT OF INTEREST
The authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
Aggregated data is presented in the SI and will be made available upon request from the corresponding author. The raw data used in this research is
not openly available due to privacy concerns.

892 MESHULAM ET AL.
ORCID
Tamar Meshulam https://orcid.org/0000-0002- 4642-0655
David Font-Vivanco https://orcid.org/0000-0002- 3652-0628
VeredBlass https://orcid.org/0000-0003- 0939-976X
Tamar Makov https://orcid.org/0000-0001-7345- 5864
REFERENCES
Acquier, A., Daudigeos, T., & Pinkse, J. (2017). Promises and paradoxes of the sharing economy: An organizing framework. Technological Forecasting and Social
Change,125, 1–10.
Ala-Mantila, S., Ottelin, J., Heinonen, J., & Junnila, S. (2016). To each their own? The greenhouse gas impacts of intra-household sharing in different urban
zones. Journal of Cleaner Production,135, 356–367.
Amatuni, L., Ottelin, J., Steubing, B., & Mogollón, J. M. (2020). Does car sharing reduce greenhouse gas emissions? Assessing the modal shift and lifetime shift
rebound effects from a life cycle perspective. Journal of Cleaner Production,266, 121869.
Bahn-Walkowiak,B., Bleischwitz, R., Distelkamp, M., & Meyer, M. (2012). Taxing construction minerals: A contribution to a resource-efficient Europe. Mineral
Economics,25(1), 29–43.
Bjelle, E. L., Wiebe, K. S., Többen, J., Tisserant, A., Ivanova, D., Vita, G., & Wood, R. (2021). Future changes in consumption: The income effect on greenhouse
gas emissions. Energy Economics,95, 105114.
Botsman, R., & Rogers, R. (2011). What’s mine is yours: How collaborative consumption is changing the way we live. Collins.
Brookes, L. (1979). A low energy strategy for the UK. Atom,269, 3–8.
Chen, T. D., & Kockelman, K. M. (2016). Carsharing’s life-cycle impacts on energy use and greenhouse gas emissions. Transportation Research Part D: Transport
and Environment,47, 276–284.
Ciccullo, F., Fabbri, M., Abdelkafi, N., & Pero, M. (2022). Exploring the potential of business models for sustainability and big data for food waste reduction.
Journal of Cleaner Production,340, 130673. https://doi.org/10.1016/j.jclepro.2022.130673
Cheng, M., Chen, G., Wiedmann, T., Hadjikakou, M., Xu, L., & Wang, Y. (2020). The sharing economy and sustainability—Assessing Airbnb’s direct, indirect and
induced carbon footprint in Sydney. Journal of Sustainable Tourism,28(8), 1083–1099.
Chitnis, M., Sorrell, S., Druckman, A., Firth, S. K., & Jackson, T. (2013). Turning lights into flights: Estimating direct and indirect rebound effects for UK
households. Energy Policy,55, 234–250.
Chitnis, M., Sorrell, S., Druckman, A., Firth, S. K., & Jackson, T. (2014). Who rebounds most? Estimating direct and indirect rebound effects for different UK
socioeconomic groups. Ecological Economics,106, 12–32.
Clewlow, R. R., & Mishra, G. S. (2017). Disruptive transportation: The Adoption, utilization, and impacts of ride-hailing in the United States. Working Paper Series.
Institute of TransportationStudies, UC Davis. https://ideas.repec.org/p/cdl/itsdav/qt82w2z91j.html
Curtis, S. K., & Mont, O. (2020). Sharing economy business models for sustainability. Journal of Cleaner Production,266, 121519.
Davies, A., & Evans, D. (2019). Urban food sharing: Emerging geographies of production, consumption and exchange. Geoforum,99, 154–159.
Deaton, A., & Muellbauer, J. (1980). An almost ideal demand system. The American Economic Review,70(3), 15.
Diao, M., Kong, H., & Zhao, J. (2021). Impacts of transportation network companies on urban mobility. Nature Sustainability,4(6), 494–500.
Druckman, A., Chitnis, M., Sorrell, S., & Jackson, T. (2011). Missing carbon reductions? Exploring rebound and backfire effects in UK households. Energy Policy,
39(6), 3572–3581.
Duchin, F. (1992). Industrial input-output analysis: Implications for industrial ecology. Proceedings of the National Academy of Sciences of the United States of
America,89(3), 851–855.
Eurostat. (2018). Concepts for household consumption—Comparison between micro and macro approach.https://ec.europa.eu/eurostat/statistics- explained/
index.php?title=Concepts_for_household_consumption_-_comparison_between_micro_and_macro_approach
Eurostat. (2021a). HICP (2015 =100)—Annual data (average indexand rate of change).https://ec.europa.eu/eurostat/databrowser/view/prc_hicp_aind/default/
table?lang=en
Eurostat. (2021b). Final consumption expenditure of households by consumption purpose (COICOP 3 digit).https://ec.europa.eu/eurostat/databrowser/view/
NAMA_10_CO3_P3__custom_569355/default/table?lang=en
Falcone, P. M., & Imbert, E. (2017). Bringing a sharing economy approach into the food sector: The potential of food sharing for reducing food waste. In:
Morone, P., Papendiek, F., Tartiu, V. (eds) Food waste reduction and valorisation: Sustainability assessment and policy analysis, (pp. 197–214.). Springer, Cham.
https://doi.org/10.1007/978-3-319- 50088-1_10
FAO. (2013). Food wastage footprint: Impacts on natural resources: Summary report. Food and Agriculture Organization of the United Nations.
FAO. (2018). Food loss and waste and the right to adequate food: Making the connection. Food and Agriculture Organization of the United Nations. http://www.
fao.org/3/ca1397en/CA1397EN.pdf
Font-Vivanco, D., Freire-González, J., Kemp, R., & van der Voet, E. (2014). The remarkable environmental rebound effect of electric cars: A microeconomic
approach. Environmental Science & Technology,48(20), 12063–12072.
Font-Vivanco, D., & Makov, T. (2020). The role of technology and rebound effects in the success of the sustainable development goals framework. In Adenle, A. A.,
Chertow, M. R., Moors, E. H. M., & Pannell, D. J.(2020). Science, Technology,and Innovation for Sustainable Development Goals: Insights from Agriculture,
Health, Environment, and Energy (pp. 192–216.). Oxford University Press.
Font-Vivanco, D., McDowall, W., Freire-González, J., Kemp, R., & van der Voet, E. (2016). The foundations of the environmental rebound effect and its
contribution towards a general framework. Ecological Economics,125, 60–69.
Freire-González, J., & Font-Vivanco, D. (2017). The influence of energy efficiency on other natural resources use: An input-output perspective. Journal of
Cleaner Production,162, 336–345.
Frenken, K., & Schor, J. (2017). Putting the sharingeconomy into perspective. Environmental Innovation and Societal Transitions,23, 3–10.
Galbraith, K. (2012). The battle against food waste. The New York Times.https://www.nytimes.com/2012/01/16/business/global/the-battle-against-food-
waste.html

MESHULAM ET AL.893
Gallo, G. (2021). Surplus food redistribution and rebound effect. The case study of Denmark, Copenhagen University. https://www.researchgate.net/publication/
354862896_Surplus_food_redistribution_and_rebound_effectThe_case_study_of_Denmark_Sweden_and_Italy
Galvin, R. (2020). Who co-opted our energy efficiency gains? A sociology of macro-level rebound effects and US car makers. Energy Policy,142, 111548.
Gehrke, S. R., Felix, A., & Reardon, T. G. (2019). Substitution of ride-hailing services for more sustainable travel options in the greater Boston region.
TransportationResearch Record,2673(1), 438–446.
Gillies, S. & others. (2007). Shapely: Manipulation and analysis of geometric objects.https://github.com/Toblerity/Shapely
Gillingham, K., Kotchen, M. J., Rapson, D. S., & Wagner, G. (2013). The rebound effect is overplayed. Nature,493(7433), 475–476.
Gong, J., Greenwood, B. N., & Song, Y. (2017). Uber might buy me a Mercedes Benz: An empirical investigation of the sharing economy and durable goods
purchase. SSRN Electronic Journal,48.
Grabs, J. (2015). The rebound effects of switching to vegetarianism. A microeconomic analysis of Swedish consumption behavior. Ecological Economics,116,
270–279.
Greening, L. A., Greene, D. L., & Difiglio, C. (2000). Energy efficiency and consumption—The rebound effect—A survey. Energy Policy,28(6), 389–401.
Gur˘
au, C., & Ranchhod, A. (2020). The sharing economy as a complex dynamic system: Exploring coexisting constituencies, interests and practices. Journal of
Cleaner Production,245, 118799.
Hagedorn, W., & Wilts, H. (2019). Who should waste less? Food waste prevention and rebound effects in the context of the Sustainable Development Goals.
GAIA—Ecological Perspectives for Science and Society,28(2), 119–125.
Hamari, J., Sjöklint, M., & Ukkonen, A. (2016). The sharing economy: Why people participate in collaborative consumption. Journal of the Association for
Information Science and Technology,67(9), 2047–2059.
Hanbury, H., Bader, C., & Moser, S. (2019). Reducing working hours as a means to foster low(er)-carbon lifestyles? An Exploratory Study on Swiss Employees.
Sustainability,11(7), 2024.
Harvey, J., Smith, A., Goulding, J., & Illodo, I. B. (2020). Food sharing, redistribution, and waste reduction via mobile applications: A social network analysis.
Industrial Marketing Management,88, 437–448.
Heinrichs, H. (2013). Sharing economy: A potential new pathway to sustainability. GAIA—Ecological Perspectives for Science and Society,22(4), 228–231.
Henningsen, A. (2017). Demand analysis with the almost ideal demand system in R: Package micEconAids. Cran.Microsoft.Com, 36.
https://www.sciencedirect.com/science/article/pii/S0959652619335383?casa_token=nibRryoJR1sAAAAA:OzjdTmp48aXB3M1uFg9-
K8xQxgNjaRKmPZgZImhVFKIaWW0DOk28z7o4bHb8-3KRjAhIN- tmxA
Henry, M., Schraven, D.,Bocken, N., Frenken, K., Hekkert, M., & Kirchherr, J.(2021). The battle of the buzzwords: A comparative review of the circular economy
and the sharing economy concepts. Environmental Innovation and Societal Transitions,38, 1–21.
Hertwich, E. G. (2005). Consumption and the rebound effect: An industrial ecology perspective. Journal of Industrial Ecology,9(1–2), 85–98.
Horn, K., & Merante, M. (2017). Is home sharing driving up rents? Evidence from Airbnb in Boston. Journal of Housing Economics,38, 14–24.
Ivanova, D., Stadler, K., Steen-Olsen, K., Wood, R., Vita, G., Tukker, A., & Hertwich, E. G. (2016). Environmental impact assessment of household consumption.
Journal of Industrial Ecology,20(3), 526–536.
Jevons, W. S. (1865). The Coal Question:An inquiry concerning the progress of the nation, and the probable exhaustion of our coal-mines. Macmillan.
Jordahl, K. (2014). GeoPandas: Python tools for geographic data.https://Github.Com/Geopandas/Geopandas
Joshi, S. (1999). Product environmental life-cycle assessment using input-output techniques. Journal of Industrial Ecology,3(2–3), 95–120.
Jung, J., & Koo, Y. (2018). Analyzing the effects of car sharing services on the reduction of greenhouse gas (GHG) emissions. Sustainability,10(2), 539.
Kerdlap, P., Gheewala, S. H., & Ramakrishna, S. (2021). To rent or not to rent: A question of circular prams from a life cycle perspective. Sustainable Production
and Consumption,26, 331–342.
Khazzoom, J. D. (1980). Economic implications of mandated efficiency in standards for household appliances. The Energy Journal,1(4), 21–40.
Laukkanen, M., & Tura,N. (2020). The potential of sharing economy business models for sustainable value creation. Journal of Cleaner Production,253, 120004.
Lee, K., Jin, Q., Animesh, A., & Ramaprasad, J.(2019). Ride-hailing services and sustainability: Theimpact of Uber on the transportation mode choices of drivers, riders,
and walkers. SSRN Scholarly Paper. Social Science Research Network. https://papers.ssrn.com/abstract=3244207
Leontief, W. (1970). Environmental repercussions and the economic structure: An input-output approach. The Review of Economics and Statistics,52(3), 262–
271.
Ma, Y., Lan, J., Thornton, T., Mangalagiu, D., & Zhu, D. (2018). Challenges of collaborative governance in the sharing economy: The case of free-floating bike
sharing in Shanghai. Journal of Cleaner Production,197, 356–365.
Mair, J., & Reischauer, G. (2017). Capturing the dynamics of the sharing economy: Institutional research on the plural forms and practices of sharing economy
organizations. Technological Forecasting and Social Change,125, 11–20.
Makov, A. S., Krones, J., Gupta, C., & Chertow, M. (2020). Social and environmental analysisof food waste abatement via the peer-to-peer sharing economy.
Nature Communications,11(1), 1156.
Makov, T., & Vivanco, D. F. (2018). Does the circular economy grow the pie? The case of reboundeffects from smartphone reuse. Frontiers in Energy Research,
6,https://doi.org/10.3389/fenrg.2018.00039
Manshadi, V., & Rodilitz, S. (2022). Online policies for efficient volunteer crowdsourcing. Management Science,https://pubsonline.informs.org/doi/abs/10.
1287/mnsc.2021.4220
Martin, M., Lazarevic, D., & Gullström, C. (2019). Assessing the environmental potential of collaborative consumption: Peer-to-peer product sharing in
Hammarby Sjöstad, Sweden. Sustainability,11(1), 190.
Martinez-Sanchez, V., Tonini, D., Møller, F., & Astrup, T. F. (2016). Life-cycle costing of food waste management in Denmark: Importance of indirect effects.
Environmental Science & Technology,50(8), 4513–4523.
Mazzucchelli, A., Gurioli, M., Graziano, D., Quacquarelli, B., & Aouina-Mejri, C. (2021). How to fight against food waste in the digital era: Key factors for a
successful food sharing platform. Journal of Business Research,124, 47–58.
Meshulam, T., Goldberg, S., & Makov, T. (2022). The environmental impacts of the sharing economy: A systematic review and synthesis of existing empirical
evidence.
Michelini, L., Grieco, C., Ciulli, F., & Leo, A. D. (2020). Uncovering the impact of food sharing platform business models: A theory of change approach.British
Food Journal,122(5), 1437–1462.

894 MESHULAM ET AL.
Miller, R. E., & Blair, P. D. (2009). Input–output analysis: Foundations and extensions. (2nd ed.) Cambridge University Press. https://www.cambridge.org/core/
books/inputoutput-analysis/69827DA658E766CD1E17B1A47BA2B9C3
Ministry of Housing, Communities and Local Government. (2019). File 7: All ranks, deciles and scores for the indices of deprivation, and population denominators.
https://www.gov.uk/government/statistics/english-indices-of-deprivation- 2019
Minx, J. C., Wiedmann, T., Wood, R., Peters, G. P., Lenzen, M.,Owen, A., Scott, K., Barrett, J., Hubacek, K., Baiocchi, G., Paul, A., Dawkins, E., Briggs, J., Guan, D.,
Suh, S., & Ackerman, F. (2009). Input–output analysis and carbon footprinting: An overview of applications. Economic Systems Research,21(3), 187–216.
Murray, C. K. (2013). What if consumers decided to all ‘go green?’ Environmental rebound effects from consumption decisions. Energy Policy,54, 240–256.
Neslen, A. (2016). Action to cut food waste gains momentum across Europe. The Guardian,https://www.theguardian.com/environment/2016/jul/13/action-
to-cut- food-waste- gains-momentum-across- europe
Nijland, H., & van Meerkerk, J. (2017). Mobility and environmental impacts of car sharing in the Netherlands.Environmental Innovation and Societal Transitions,
23, 84–91.
ONS. (2021). Input–output supply and use tables—Summary tables.https://www.ons.gov.uk/economy/nationalaccounts/supplyandusetables/datasets/
inputoutputsupplyandusetablessummarytables
ONS. (2022). Households’ saving ratio.https://www.ons.gov.uk/economy/grossdomesticproductgdp/timeseries/dgd8/ukea
Parfitt, J., Barthel, M., & Macnaughton, S. (2010). Food waste within food supply chains: Quantification and potential for change to 2050. Philosophical
Transactionsof the Royal Society B: Biological Sciences,365(1554), 3065–3081.
Richards, T. J., & Hamilton, S. F. (2018). Food waste in the sharing economy. FoodPolicy,75, 109–123.
de Ruiter, H., Macdiarmid, J. I., Matthews, R. B., Kastner, T., & Smith, P. (2016). Global cropland and greenhouse gas impacts of UK food supply are increasingly
located overseas. Journal of The Royal Society Interface,13(114), 20151001.
Salemdeeb, R., Font Vivanco, D.,Al-Tabbaa, A., & zu Ermgassen, E. K. H. J. (2017). A holistic approach to the environmental evaluation of food waste prevention.
Waste Management,59, 442–450.
Schleich, J., Mills, B., & Dütschke, E. (2014). A brighter future? Quantifying the rebound effect in energy efficient lighting. Energy Policy,72, 35–42.
Schor, J. (2014). Debating the sharing economy. Journal of Self-Governance and Management Economics,4(3), 7.
Schor, J. (2020). The shared economy is a lie. In After the gig: How the sharing economy got hijacked and how to win it back(pp. 105–121). University of California
Press.
Sorrell, S. (2007). The Rebound effect: An assessment of the evidence for economy-wide energy savings from improved energy efficiency. Areportproduced
by the Sussex Energy Group for the Technology and Policy Assessment function of the UK Energy. UK Energy Research Centre .
Sorrell, S., Gatersleben, B., & Druckman, A. (2020). The limits of energy sufficiency: A review of the evidence for rebound effects and negative spillovers from
behavioural change. Energy Research & Social Science,64, 101439.
Stadler, K. (2021). Pymrio—A Python based multi-regional input-output analysis toolbox. Journal of Open Research Software,9(1), 8.
Stadler, K., & Didier. (2020). .Konstantinstadler/pymrio: Bugfix release. Zenodo.https://zenodo.org/record/4281378
Stadler, K., Wood, R., Bulavskaya, T., Södersten, C.-J., Simas, M., Schmidt, S., Usubiaga, A., Acosta-Fernández, J., Kuenen, J., Bruckner, M., Giljum, S., Lutter, S.,
Merciai, S., Schmidt, J. H., Theurl, M. C., Plutzar, C., Kastner, T., Eisenmenger, N., Erb, K.-H., . .. Tukker, A. (2018). EXIOBASE 3: Developing a time series of
detailed environmentally extended multi-regional input-output tables. Journal of Industrial Ecology,22(3), 502–515. https://doi.org/10.1111/jiec.12715
Statista. (2020). Value of the global sharing economy 2014–2025. Statista,https://www.statista.com/statistics/830986/value-of- the-global-sharing-
economy/
Thiesen, J., Christensen, T., Kristensen, T., Andersen, R., Brunoe, B., Gregersen, T., Thrane, M., & Weidema, B. (2008). Rebound effects of price differences. The
International Journal of Life Cycle Assessment,13(2), 104–114.
Tukker, A., Goldbohm, R. A., Koning, A. D., Verheijden, M., Kleijn, R., Wolf, O., Perez-Dominguez, I., & Rueda-Cantuche, J. M. (2011). Environmental impacts of
changes to healthier diets in Europe. Ecological Economics,70(10), 1776–1788.
Tukker, A., Bulavskaya, T., Giljum, S., de Koning, A., Lutter, S., Simas, M., Stadler, K., & Wood, R. (2016). Environmental and resource footprints in a global
context: Europe’s structural deficit in resource endowments. Global EnvironmentalChange,40, 171–181. https://doi.org/10.1016/j.gloenvcha.2016.07.002
Tussyadiah, I. P., & Pesonen, J. (2015). Impacts of peer-to-peer accommodation use on travel patterns. Journal of Travel Research,55, 1022–1040. https://
journals.sagepub.com/doi/10.1177/0047287515608505
Tussyadiah, I. P., & Pesonen, J. (2018). Drivers and barriers of peer-to-peer accommodation stay—An exploratory study with American and Finnish travellers.
Current Issues in Tourism,21(6), 703–720.
UK Office for National Statistics. (2021). Family spending workbook 1: Detailed expenditure and trends. https://www.ons.gov.uk/
peoplepopulationandcommunity/personalandhouseholdfinances/expenditure/datasets/familyspendingworkbook1detailedexpenditureandtrends
UNEP DTU Partnership and United Nations Environment Programme. (2021). Reducing consumer food waste using green and digital technologies.https://
unepdtu.org/wp-content/uploads/2021/11/reducing- consumer-food- waste-using-green- and-digital- technologies.pdf
United Nations Environment Programme. (2021). Food Waste Index Report 2021.http://www.unep.org/resources/report/unep-food-waste- index-report-
2021
U.S. Environmental Protection Agency. (2020). 2018 Wasted Food Report.
Vanham,D., Bouraoui, F., Leip,A., Grizzetti, B., & Bidoglio, G. (2015). Lost water and nitrogen resources due to EU consumer food waste. Environmental Research
Letters,10(8), 084008.
Vélez-Henao, J.-A., García-Mazo, C.-M., Freire-González, J., & Vivanco, D. F. (2020). Environmental rebound effect of energy efficiency improvements in
Colombian households. Energy Policy,145, 111697.
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J.,
Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., .. . SciPy 1.0. Contributors. (2020). SciPy 1.0: Fundamental
algorithms for scientific computing in Python. Nature Methods,17, 261–272.
Warmington-Lundström, J., & Laurenti, R. (2020). Reviewing circular economy rebound effects: The case of online peer-to-peer boat sharing. Resources,
Conservation & Recycling: X,5, 100028.
Wasserbaur, R., Sakao, T., Söderman, M. L., Plepys, A., & Dalhammar, C. (2020). What if everyone becomes a sharer? A quantification of the environmental
impact of access-based consumption for household laundry activities. Resources, Conservation and Recycling,158, 104780.
WRAP. (2013). Household food and drink waste in the United Kingdom 2012. Waste and Resource Action Programme. Banbury, Oxon.

MESHULAM ET AL.895
WRAP. (2014). Econometric modelling and household food waste. Waste and Resources Action Programme, Banbury, Oxon.
WRAP. (2020). UK progress against Courtauld 2025 targets and UN Sustainable Development Goal 12.3.https://wrap.org.uk/resources/report/uk-progress-
against-courtauld- 2025-targets- and-un-sustainable- development-goal- 123
WWF-UK. (2021). Driven to waste: The global impact of food loss and waste on farms.https://wwf.panda.org/discover/our_focus/food_practice/food_loss_and_
waste/driven_to_waste_global_food_loss_on_farms.cfm
SUPPORTING INFORMATION
Additional supporting information can be found online in the Supporting Information section at the end of this article.
How to cite this article: Meshulam, T., Font-Vivanco, D., Blass, V., & Makov, T. (2023). Sharing economy rebound: The case of peer-to-peer
sharing of food waste. Journal of Industrial Ecology,27, 882–895. https://doi.org/10.1111/jiec.13319