Appendix D
Interventions to Reduce Food Waste at the Consumer Level: Examples from the Literature
This appendix presents examples of selected studies that the committee reviewed to assess the effectiveness of interventions to reduce food waste at the consumer level. To provide context for the examples, which are presented in boxes D-1 through D-13, the text from Chapter 4 that summarizes each intervention type is repeated here. At the end of the examples, Table D-1 summarizes all of the intervention studies, grouped by one of two tier levels and setting. Tier 1 studies met four criteria: an intervention was implemented; wasted food was measured; causal effect can be attributed; statistical analysis was adequate; tier 2 studies failed to meet at least one of the four criteria. The settings in the studies were universities, schools, restaurants, retail establishments, and households.
The studies in this appendix are organized by type of intervention, paralleling the structure in Chapter 4. Interventions were selected for description in the boxes based on their ability to inform understanding of the intervention type or to provide ideas for future research and interventions. Most studies include more than one intervention type, and in a few cases the committee opted to discuss a study twice, highlighting different aspects of it in separate examples.
Table D-1 provides a comprehensive overview of the studies meeting our inclusion criteria, though not all of them are covered in the boxes; the table also includes a handful of modeling studies. Although they are based on assumptions and less on empirical data, modeling studies are useful in that they explore not only the effect of interventions on wasted food, but
also effects on other variables of interest. Therefore, they can be particularly well designed to explore potential systems-level effects. Description of the literature search process can be found in Appendix B.
The summary and conclusions from the committee’s review is presented in Chapter 4, which also presents the criteria that the committee used to assess the quality of the studies and to group by tier levels.
APPEALS
Appeal interventions encourage consumers to change their behavior to achieve a social benefit. Explicit appeals, which request action directly, are distinct from implicit appeals, which do not make a request. Implicit appeals may be based on a presumption that the facts will tap into existing attitudes or values, or may serve as prompts to action by raising awareness. Explicit appeals build on those mechanisms, and also activate the human tendency to respond to requests, particularly when they align with values, when the requestor is valued, or when something is owed to the requestor (reciprocity). Twenty-five of the 64 studies reviewed by the committee included appeal interventions, including 13 which used explicit appeals (Box D-1), 3 which used implicit appeals (Box D-2), and 9 that used both and other intervention types (Box D-3). The largest number of interventions presented signage or other messaging in food service venues, often in universities. Other interventions provided messages directly to study participants, or engaged participants in creating messages; one pair of studies involved delivering messages to the general public.
One tier 1 study (Ellison et al., 2019; United States) found a null effect for the appeal component, and one found an overall null intervention effect (Liz Martins et al., 2016; Portugal), but it was not possible to isolate the appeal component. All but three of the tier 2 studies found statistically significant impacts, with the magnitude of effect varying. A few tier 2 studies involved comparing appeal interventions with other types, such as providing information (Collart and Interis, 2018, United States), and feedback (Whitehair et al., 2013, United States) with results favorable to appeal interventions. In at least a quarter of the studies it was not possible to disentangle the results of the appeal intervention from those of other interventions included in the study. Few studies looked at maintenance of impact across time.
ENGAGEMENT
Engagement interventions change psychological processes by engaging the consumer in, for example, setting goals, establishing implementation intentions, making a commitment, or increasing mindfulness toward the target behavior. Some examples of interventions are in Box D-4. Twelve studies (six in tier 1) feature such interventions, which are often multifaceted, operating through multiple drivers. Thus, the results of this type of intervention may be manifested in a variety of ways. These interventions have a mixed record in delivering significant reductions in food waste, which makes it difficult to provide a summary evaluation. For example, engagement interventions delivered in the home included diverse mechanisms: systematic engaging individuals to reconsider household food routines (Devaney and Davies 2017, tier 2, Ireland); providing tools to support changes in meal planning or preparation (Romani et al., 2018, tier 1, Italy);
and using gamification to accelerate and deepen learning about wasted food (Soma et al., 2020, tier 1, Canada). Several food service interventions were also comprehensive, involving both food service personnel and patrons (Strotmann et al., 2017, tier 2, Germany) or both food service personnel and student customers (Prescott et al., 2019, tier 1, United States).
The results of these studies suggest that interventions aimed at reprogramming base processes that drive food waste hold promise, but the lack of consistent reductions implies that formulating the multiple elements common to this approach may be difficult. Furthermore, the complex and multifaceted nature of these interventions impedes assessment of which individual strategy or subset of strategies drives efficacy.
SOCIAL COMPARISON
Social comparison interventions operate on principles of social influence. Some examples of interventions are in Box D-5. Twelve studies, all tier 2, included such interventions. The interventions studied were diverse, focusing on social desirability, public commitment, social media communications, communication of social norms, food sharing, and such situations as workshops in which a peer group might influence behavior. The authors of only three of these studies provide quantitative results that make it possible to distinguish the effects of the social comparison intervention from those of other interventions in the study. Two of these three focused on restaurant leftovers. Stockli and colleagues (2018, Switzerland) and Hamerman and colleagues (2018, United States) found that messages designed to invoke social norms (i.e., saying a majority of patrons request to take food home) were not more effective than informative messages. Hamerman and colleagues (2018) found that study participants were significantly more likely to request to take home leftovers when they envisioned dining with friends versus dining with someone they wanted to impress. Five of the studies used qualitative or mixed methods approaches, with all but one suggesting that social comparison was beneficial in preventing waste. Findings from Lazell (2016, United Kingdom) echo those from Hamerman et al. (2018 United States) suggesting that the effectiveness of social comparison interventions can depend on participants’ views about what behavior is normative, and about the social groups with which they are comparing themselves.
Overall, the evidence regarding social comparison interventions is inconclusive, and the research suggests a need for nuanced intervention development and careful selection of social groups for comparison and messaging.
FEEDBACK
Feedback interventions shape targeted behaviors by providing information that reinforces or corrects those behaviors. Some examples of interventions are in Box D-6. Seven of the studies reviewed (three tier 1) featured feedback interventions, largely as part of multifaceted interventions implemented in food service settings. Thus, it was difficult to identify the independent impact of the feedback strategies. A common strategy was to offer cafeteria patrons feedback concerning the average waste created by other
patrons, although studies using such strategies as part of a multifaceted intervention revealed little success. Personalized feedback, often generated for elementary and middle school students in cafeteria settings as part of a multifaceted intervention, showed some statistically significant effects (e.g., Liz Martins et al., 2016, tier 1, Portugal; Prescott et al., 2019, tier 1, United States). Feedback delivered among different food service worker stations within a large hospital facility showed promise as part of a multifaceted intervention that significantly reduced waste (Strotmann et al., 2017, tier 2, Germany). And a qualitative assessment of the use of home cameras to track waste suggests that such approaches could stimulate waste reduction by invoking feelings of shame (Comber and Thieme, 2013, tier 2, United States). Overall, feedback interventions have a mixed record, with weaker effects when feedback is not individualized.
FINANCIAL INCENTIVES
Interventions providing financial incentives alter the monetary consequences of behaviors that can influence the amount of food consumers waste. One tier 1 study in South Korea found that financial penalties that increase with amount of wasted food generated at the household level are more effective at reducing the amount of wasted food than financial penalties tied to community level waste amounts (Lee and Jung, 2017). It has been well documented that overall household waste disposal (food plus nonfood waste) declines when households are forced to pay more for additional amounts of waste (Bel and Gradus, 2016). Nine studies (all tier 2) featured financial interventions. Some examples are in Box D-7. Most involved comparing the effects of retail price reductions with those of other approaches used to encourage consumers to purchase suboptimal (ugly or expired) food that might otherwise be wasted. These studies yielded statistically significant evidence that price reductions can increase purchase intentions. However, alternative motivational approaches, such as highlighting
the environmental consequences of food waste, often yielded changes similar to those seen in purchase intentions or enhanced the effectiveness of price discounts.
Two studies focused on quantity (e.g., large pack or multipack) (Le Borgne et al., 2018, tier 2, France; Petit et al., 2019, tier 2, United States). These studies showed that giving consumers information about how such deals can translate to greater waste had less effect on purchase intentions relative to simply lowering unit costs for certain foods. Two studies in food service settings showed mixed results when comparing the efficacy of imposing fines for excessive plate waste with that of emphasizing environmental benefits to reduce plate waste (Chen and Jai, 2018, tier 2, United States; Kuo and Shih, 2016, tier 2, Taiwan).
Overall, financial incentives are a promising way to discourage behaviors that are precursors to food waste and to increase motivation for overall home waste reduction. However, linking financial incentives to decision points specific to wasting food may prove difficult, and establishing efficacy and implementation feasibility will require considerable additional research.
NUDGES
Nudge interventions alter the choice architecture faced by consumers in a manner designed to encourage targeted behaviors without engaging conscious (reflective) decision making (see Chapter 1). The committee reviewed 24 studies (four tier 1) that involved such interventions, most of which addressed food service settings. The nudge interventions studied operated by means of diverse mechanisms, including shifting perceived quantity, altering appeal, or changing the default/easiest action. The interventions assessed in about 40 percent of the studies focused on shifting consumers’ perceptions of quantity through changes to portion size, package size, plate size, or tray availability (see examples in Box D-8). Most of the studies found significant reductions in waste attributable to quantity manipulations, although only two such studies were tier 1. Three studies in the United States (Kim and Morawski, 2013, tier 1; Sarjahani et al., 2009, tier 2; Thiagarajah and Getty, 2013, tier 2) focused on removal of cafeteria trays, which limits quantity by making it more difficult for patrons in buffet settings to carry multiple plates. All three of these studies (plus several non-peer-reviewed) found significant reductions in plate waste. In contrast, one recent non-peer-reviewed literature study (Cardwell et al., 2019) found no effect.
Another 40 percent of studies involved altering the appeal of food with the intent of decreasing waste by encouraging increased consumption. Several tier 2 studies enhanced appeal directly by improving meal quality or better matching meal components to patron preferences. Box D-9 provides examples of those studies, the majority of which showed a significant reduction in waste for these interventions.
Other studies, including two tier 1 studies (Ilyuk, 2018, United States; Williamson et al., 2016, United States) involved nudges to increase appeal less directly, including by altering the quality of the material of the plate used; providing priming messages to subtly enhance the self-esteem of customers considering the purchase of suboptimal foods; making purchasing require more effort to enhance the consumer’s psychological ownership of food; and providing cafeteria meals after recess, when student appetite would be greater. Box D-10 provides examples of four of these studies, all of which found significant effects.
The remaining studies (all tier 2) involved forcing changes to consumers’ default behaviors (see examples in Box D-11). Two studies focused on date labels, with one altering descriptive phrases (e.g., changing “sell by” to “use by”) to stimulate different processing of date information (no effect) and the other removing dates to force different evaluation approaches for product freshness (significant reduction).
One study (Manzocco et al. (2017, tier 2, Italy) considered how lowering ambient refrigerator temperatures might help consumers discard less produce and elicited consumer-intended discard of salad packages that were maintained under different refrigeration conditions (see also below for modeling studies that highlight the potential benefits of improving refrigeration technology). Extending the time period at which food remains at peak quality is among the most promising approaches to preventing waste at all levels of the food supply chain, and such approaches have particular utility for helping consumers navigate scheduling shifts that prevent using purchased food when planned. Although considerable technological design effort exists in that space, such as packaging, including modeling studies assessing potential impacts, they are seldom tested in interventions that
specifically assess the impact on consumer discards; and thus other studies did not qualify for this review.
Policies that ban organic waste from landfills can also change default behaviors (Sandson and Broad Leib, 2019) although none of the studies reviewed examined such interventions.
Overall, the empirical support of nudge interventions focused on shifting food quantity and appeal is the stronger than that for any of the other intervention types with statistically significant effect sizes being documented in multiple studies of this intervention type. However, the evidence is mixed, dominated by tier 2 studies, and limited in context (studies of nudges were primarily short-run evaluations carried out in buffet settings). Further, the potential for these interventions to be feasible needs to be considered in light of effects of the COVID-19 pandemic, such as how the closing of food service venues during the pandemic will affect other practices related to food.
INFORMATION
One of the most common and seemingly intuitive approaches to addressing food waste is providing participants with concrete advice aimed at helping them reduce their waste: a tool for action, such as knowledge or skills regarding how to reduce waste. This category is distinct from appeal and feedback interventions, which also provide forms of information; information interventions entail providing only “how-to” information. Intervention designs of this type are often rooted in the theory of planned behavior (see Chapter 1).
The committees’ literature search identified 22 studies that included information interventions, three of which are tier 1 studies (see examples in Box D-12). The interventions studied were fairly evenly divided between household and food service settings. In most cases, the guidance provided included multiple how-to tips targeting different strategies for reducing food waste or preserving food longer. The information and tools provided were often designed to be proximate to the point of decision making (e.g., refrigerator magnets and food containers for storage decisions, spreadsheets for use when planning meals). Advice was provided in a variety of modalities, from pamphlets and information packets to films, signage, and social media.
In most cases, the information interventions paired advice with other interventions, such as calls to action, tracking, or communication of social norms. Thus in many of the studies (8 of the 22, including 2 of the 3 tier 1 studies (Liz Martins et al., 2016; Portugal; van der Werf et al., 2019, Canada), it was not possible to distinguish the effects of the information component itself. The third tier 1 study (Soma et al., 2020, Canada) showed
a small effect for the information component when the intervention encouraged participants to engage actively with the information through quizzes with rewards, while passive participation or modes that required more coordination to achieve engagement (attending group workshops) failed to produce significant waste reduction.
Six of the tier 2 studies found significant positive effects that could be attributed directly to the information provision. One involved tailoring the information provided based on pretest results, a procedure that significantly improved outcomes (Schmidt, 2016, Germany). Two studies found null effects of the information provision (Ahmed et al., 2018, United States; Jagau and Vvrastekova, 2017, The Netherlands). In some cases, the effects measured reflected intermediate outcomes, such as knowledge. Qualitative studies generally found positive effects for providing information through such means as intensive small group sessions. The committee also reviewed two studies (tier 2) where a U.K. retailer implemented multiple informational and social approaches using communication techniques, with positive effects on food waste (Young et al., 2017, 2018). Several other reports of large-scale information interventions that had not been peer reviewed also suggested potential positive impacts for information interventions.
In summary, while some studies suggest significant effects may be achieved with simple informational interventions alone, other studies suggest null effects, and long-term impacts must be assessed. Additionally, as the public grows more knowledgeable about wasted food, the impact of informational approaches may be reduced.
MODELING STUDIES
While high-quality empirical evaluations are critical for providing robust recommendations concerning the effectiveness of interventions to reduce wasted food at the consumer level, studies that develop, calibrate, and simulate models of consumer behavior (modeling studies) can also provide important insights concerning the potential effectiveness of individual interventions or suites of interventions. Given the burden of implementation and tracking, most intervention studies focus on a single stage in the consumer process (e.g., purchase, home meal preparation, consumption, discard) rather than systems-level interventions. Modeling studies can provide insights into systems-level spillovers that might occur in response to interventions, including predictions concerning behavioral and organizational responses that occur at other points in the food supply chain and the associated costs and benefits. Modeling studies generally rely on empirical work for calibration, and hence the insights generated are circumscribed by the validity of those empirical efforts. Still, they are often critical in order to connect narrow and potentially fragmented empirical efforts into a systems vision that permits broader assessment and evaluation of interventions. Box D-13 describes the modeling studies the committee reviewed: unless otherwise mentioned, these studies did not feature primary data collection.
TABLE D-1 Studies on Interventions to Reduce Food Waste at the Consumer Level, by Tier and Setting
| Study | Findings | Intervention | Intervention Types and Driversa | Limitations |
|---|---|---|---|---|
| Setting: University | ||||
| Tier 1b | ||||
| Ellison et al., 2019 (United States) |
Campaign had no significant effect on food waste but changed beliefs related to food waste |
Multifaceted with four elements (5 weeks):
|
Intervention Types
Drivers A-Knowledge C-Waste vs. other goals D-Lack of awareness/monitoring E-Psychological distance |
Unable to unpack individual effects of each intervention element |
| Kim and Morawski, 2013 (United States) |
Tray removal had a significant effect size on food waste | Removal of trays from the cafeteria (6 days) |
Intervention Types
Drivers A-Knowledge |
Exclusion of breakfast, which often features less waste, and could bias effect size upward; excludes patrons who did not use a tray even though one was available, which could bias effect size upward; does not measure long-run acclimation to trayless dining |
| Qi and Roe, 2017 (United States) |
Both information interventions with buffet diners yield significant reductions in plate waste |
2 × 2 (several sessions) intervention design:
|
Intervention Types
Drivers C-Waste vs. other goals E-Psychological distance |
Short-run assessment only; limited food menu items |
| Williamson et al., 2016 (United States) |
More food served on disposable (i.e., paper) plates is wasted than when the same food is served on permanent (i.e., hard plastic) plates. This effect persists when instead of being served a fixed quantity of food, participants select the amount and type of food |
Replacing paper with plastic plates in free buffet settings in five experimental conditions, two laboratory and three field studies:
(1) classroom (2) online survey (3A) university dining hall (3B) university dining hall (3C) high school cafeteria |
Intervention Types
Drivers A- Knowledge E-Psychological distance |
Study at universities (3A and 3B) had different limitations: study 3A plate material was confounded with food options served; study 3B captured amount wasted but not amount taken; both featured small samples (n ~ 40); no study featured the same limitation and all converged to similar magnitude of effect size |
| Study | Findings | Intervention | Intervention Types and Driversa | Limitations |
|---|---|---|---|---|
| Tier 2b | ||||
| Chen and Jai, 2018 (United States) |
Environmentally focused messages had a greater favorable influence on consumer attitudes toward food waste prevention than a threat-focused message; a higher level of perceived social corporate responsibility increased intentions to reduce food waste; perceived social corporate responsibility moderated the relationship between attitudes toward food waste messaging and the intention to reduce food waste |
Information (3 months) in hypothetical buffet dining scenario (student and faculty survey) in a 2 × 2 intervention design:
|
Intervention Types
Drivers A-Knowledge D-Lack of awareness/monitoring E-Psychological distance I- Psychosocial factors |
Criteria not met: wasted food not measured; other limitations: sample limited to university students and staff |
| Freedman and Brochado, 2010 (United States) |
Portion size was positively correlated with consumption per diner and plate waste; total amount produced in the kitchen was positively correlated with plate waste | Intervention decreased the portion size of French fries (plate size was 88g and decreased through weeks 2-5) |
Intervention Types
Drivers: A-Knowledge F-Dietary differences |
Criteria not met: no control group |
| Jagau and Vyrastekova, 2017 (The Netherlands) |
Consumers were willing to pay the same price for less food more often during the campaign than before the campaign, but the approximated impact on food waste was not significant | Information campaign (3 weeks) with banners, posters, and a recommendation to ask for a smaller portion if consumers expect not to finish the meal portion; designed to avoid consumers’ insufficient planning problem |
Intervention Types
Drivers A-Knowledge |
Criteria not met: no control group |
| Kuo and Shih, 2016 (Taiwan) |
Overall average plate waste was slightly reduced with the information intervention and reduced dramatically with the coercion intervention |
3-week longitudinal design:
|
Intervention Types
Drivers A-Knowledge D-Lack of awareness/monitoring |
Criteria not met: no control group; statistical significance not assessed |
| Study | Findings | Intervention | Intervention Types and Driversa | Limitations |
|---|---|---|---|---|
| Lazell, 2016 (United Kingdom) |
The effects of the intervention were null due to insufficient usage of the intervention tool due to situational barriers |
Mixed method study with surveys, semistructured interviews, focus groups, and an intervention (4 months):
|
Intervention Types
Drivers A-Knowledge I-Psychosocial factors |
Criteria not met: no control group; wasted food not measured, statistical significance not assessed |
| Manomaivibool et al., 2016 (Thailand) |
The proportion of clean containers (no food waste) rose significantly; a bigger increase was seen among female students than among male students |
Multifaceted design (5 days):
|
Intervention Types
Drivers D-Lack of awareness/monitoring E-Psychological distance |
Criteria not met: no control group; other limitations: cannot unpack individual effects of each intervention element |
| Study | Findings | Intervention | Intervention Types and Driversa | Limitations |
|---|---|---|---|---|
| Pinto et al., 2018 (Portugal) |
A significant mean reduction in the waste consumption index and a significant reduction in unserved food in the kitchen |
Multifaceted design (16 days):
|
Intervention Types
Drivers D-Lack of awareness/monitoring F-Dietary differences |
Criteria not met: no control group; other limitations: cannot unpack individual effects of each intervention element |
| Sarjahani et al., 2009 (United States) |
Removing trays in an all-you-can-eat cafeteria setting had a significant effect on food waste | Removal of trays from the cafeteria (3 days) |
Intervention Types
Drivers A-Knowledge |
Criteria not met: no control group; other limitations: data collected was limited to 3 days of the week |
| Thiagarajah and Getty, 2013 (United States) |
Removing trays in an all-you-can-eat cafeteria setting had a significant effect on solid food waste; it had a nonsignificant effect on liquid waste | Removal of trays from the cafeteria (2 weeks) |
Intervention Types
Drivers A-Knowledge |
Criteria not met: no control group |
| Vermote et al., 2018 (Belgium) |
Smaller portion sizes resulted in a decrease in the total intake of French fries and the total plate waste. | Longitudinal design (2 weeks): Baseline: usual porcelain bowl of French fries served (+/-200 g) Intervention: replaced bowl with smaller volume paper bags (+/-159 g) |
Intervention Types
Drivers A-Knowledge F-Dietary differences |
Criteria not met: no control group |
| Whitehair et al., 2013 (United States) |
The point prompt-type message resulted in a reduction in food waste; addition of a more personalized feedback-based message did not stimulate an additional change beyond that of the prompt message | Longitudinal design (6 weeks): Baseline: 2 weeks, no intervention Intervention 1: 1 week, posters and table tents displayed with the following text: “Eat what you take. Don’t waste food.” Intervention 2: 1 week, same posters and table tents with detailed general waste statistics (feedback-based intervention) |
Intervention Types
Drivers A-Knowledge |
Criteria not met: no control group |
| Study | Findings | Intervention | Intervention Types and Driversa | Limitations |
|---|---|---|---|---|
| Setting: School | ||||
| Tier 1b | ||||
| Liz Martins et al., 2016 (Portugal) |
Two education interventions on soup and main dish waste in an elementary school showed mixed results: significant effects were observed in the short term for students and in the medium term for teachers |
Multifaceted design (3 months) educational intervention for students and teachers: A. For children:
B. For teachers:
|
Intervention Types
Drivers D-Lack of awareness/monitoring E-Psychological distance |
Other limitations: cannot unpack individual effects of each intervention element |
|
B. For teachers:
|
| Study | Findings | Intervention | Intervention Types and Driversa | Limitations |
|---|---|---|---|---|
| Prescott et al., 2019 (United States) |
Educational intervention for 6th graders with significant reductions of fruit and vegetable plate waste 5 months postintervention; the extent of the reduction depended on food and point in time |
Mixed methods with multifaceted design:
|
Intervention Types
Drivers A-Knowledge D-Lack of awareness/monitoring E-Psychosocial distance |
Plate waste measurements over time not taken from identical menus; control group (7th and 8th graders) was older than treatment group (6th graders); treated classrooms required teacher to be willing to participate; cannot unpack individual effects of each intervention element. |
| Williamson et al., 2016 (United States) |
More food served on disposable (i.e., paper) plates was wasted than when the same food was served on permanent (i.e., hard plastic) plates; the effect persisted when instead of being served a fixed quantity of food, participants selected the amount and type of food |
Replacing paper with plastic plates in free buffet settings in five different experiments (two laboratory and three field studies):
(1) classroom (2) online survey (3A) university dining hall (3B) university dining hall (3C) high school cafeteria |
Intervention Types
Drivers A- Knowledge E-Psychological distance |
Study 3C treatment occurred 1 month later than control measures though for the same menu items, but cannot rule out seasonal trend; no study featured the same limitation, and all converged to similar magnitude of effect size |
| Tier 2b | ||||
| Barnes and Warren, 2017 (United States) |
After MyPlate Food Group books were read once a day for 2 weeks by teachers, changes in food consumption behaviors measured by food waste were not observed, but teachers indicated changes in the preschoolers’ attitudes toward trying new foods | Preschool classroom reading (2 weeks) of MyPlate Food Group books concerning origins and benefits of certain food groups (grains, fruits, and vegetables) |
Intervention Types
Drivers E-Psychological distance |
Limited to 2- to 5-year-olds; insufficient power to detect differences; intervention was not geared toward reducing food waste |
| Bergman et al., 2004 (United States) |
Plate waste was compared between two elementary schools: plate waste in the school where recess was scheduled before lunch was significantly less than when recess was scheduled after lunch | Intervention: change in practice (10 days) with recess after school lunch Control: recess before school lunch |
Intervention Types
Drivers A-Knowledge F-Dietary differences |
Criteria not met: no new intervention tested; rather, schools with preexisting differences in scheduling recess and lunch were compared; other limitations: no pre- and post-measurement as the scheduling of recess times had always differed between these two schools; no randomization of school to treatment |
| Cohen et al., 2014 (United States) |
No overall significant difference in plate waste between intervention and control; significantly less plate waste for side items for intervention schools | Training of staff for 2 years and introduction of a healthier lunch in two Boston schools |
Intervention Types
Drivers A-Knowledge |
No measurement of plate waste pre-intervention |
| Study | Findings | Intervention | Intervention Types and Driversa | Limitations |
|---|---|---|---|---|
| Schwartz et al., 2015 (United States) |
Increased consumption (less food waste) of entrée meals and vegetables; no significant changes in consumption of milk or fruit | Implementation (36 days) of the 2010 Healthy, Hunger-Free Kids Act required by USDA to update the nutrition standards of the National School Lunch Program; new policies were implemented in the 2012-2013 school year |
Intervention Types
Drivers A-Knowledge |
Criteria not met: no control group |
| Chan et al., 2008 (United States) |
There was no difference in consumption for the 50:50 blend or the refined wheat pizza crusts | Partially substitute white whole wheat flour for refined-wheat flour in pizza crust |
Intervention Types
Drivers F-Dietary differences |
Criteria not met: no control group; other limitations: study goal was not reducing food waste |
| Setting: Restaurant | ||||
| Tier 1b | ||||
| Kallbekken and Saelen, 2013 (Norway) |
Each intervention resulted in a significant reduction in food waste |
Two different interventions (6 weeks)
|
Intervention Types
Drivers A-Knowledge I-Psychosocial factors |
Other limitations: long-term effects not assessed |
| Tier 2b | ||||
| Berkowitz et al., 2016 (United States) |
Food waste was significantly reduced in intervention compared with the baseline period; energy intake and intakes of total fat, saturated fat, cholesterol, sodium, fiber, calcium, potassium, and iron were significantly lower when both full- and reduced-size entrées were served in the worksite setting and in the restaurant setting compared with when only full-size entrées were served | Intervention (7 weeks): reduce and full size serving of food items at a noncommercial worksite cafeteria and a commercial upscale restaurant Control: only full-size entrées were offered for each entrée of the day |
Intervention Types
Drivers A-Knowledge F-Dietary differences |
Criteria not met: no control group |
| Study | Findings | Intervention | Intervention Types and Driversa | Limitations |
|---|---|---|---|---|
| Stockli et al., 2018 (Switzerland) |
Diners who were prompted asked for leftover bags more frequently than controls; diners who were prompted with an informative and a normative message did not ask for leftover bags more frequently than those prompted with information only |
Intervention design (6 weeks):
|
Intervention Types
Drivers A-Knowledge H-Marketing practices |
Criteria not met: wasted food not measured; other limitations: food was limited to pizza |
| Hamerman et al., 2018 (United States) |
Envisioning dining with others who they wanted to impress led to greater perceived likelihood of taking home leftovers when the server proactively offered to wrap the leftovers versus when this did not occur; this difference did not hold true when participants imagined dining companions with whom they were comfortable |
2 × 2 intervention design:
|
Intervention Types
Drivers F-Different preferences/diets G-Inconvenience H-Marketing practices I-Psychosocial factors |
Criteria not met: wasted food not measured; other limitations: questionnaire based, virtual restaurant |
| Wansink and van Ittersum, 2013 (United States) |
Looking at the effect of dinnerware size on plate waste, Chinese buffet diners with large plates served more, ate more, and wasted more food than those with smaller plates; educational intervention had no impact on these results |
Interventions:
|
Intervention Types
Drivers A-Knowledge F-Dietary differences |
Subjects not randomized to plate sizes; small number of observations (N = 43) |
| Kuperberg et al., 2008 (Canada) |
With room service, satisfaction increased, food costs decreased at breakfast and lunch, and reductions in waste occurred at all meals | Room service delivery system in a pediatric hospital compared with the standard a cold-plating tray delivery system where food is chosen 2 days prior and quality of the food is questionable |
Intervention Types
Drivers C-Waste vs. other goals F-Dietary differences J-Built environment |
Criteria not met: no control group |
| Study | Findings | Intervention | Intervention Types and Driversa | Limitations |
|---|---|---|---|---|
| Ahmed et al., 2018 (United States) |
Intervention led to a nonsignificant reduction in total food waste with a large portion of waste attributed to postconsumer plate waste |
Multifaceted interventions (1.5 weeks):
|
Intervention Types
Drivers A-Knowledge H-Marketing practices I-Psychosocial factors |
Criteria not met: no control; other limitations: unable to unpack individual effects of each intervention element |
| Lorenz-Walther et al., 2019 (Germany) |
Portion size reductions for target dishes were found to relate to lower levels of plate waste based on conscious perception, represented in smaller portion size ratings; effects from seeing information posters based on changed personal attitudes, subjective norms, and perceived behavioral control, but depended on how an individual reacts to the information (by only making an effort to finish all food or by making an effort and additionally choosing a different dish in the canteen); opposite effects on these determinants and consequently also on plate leftovers |
Two interventions (2 weeks):
Authors also analyzed how the display of information posters and the reduction of portion sizes effect personal, social. and environmental determinants in a structural equation model by applying data from online surveys and observations |
Intervention Types
Drivers A-Knowledge H-Marketing practices I-Psychosocial factors |
Criteria not met; no control; other limitations: disentanglement of effects of the two interventions relies on respondent survey results and statistical modeling |
| Strotmann et al., 2017 (Germany) |
The average waste rate in the residential home and in the hospital cafeteria were significantly reduced: in the hospital, the average waste rate remained constant; however, the average daily food provided and wasted per person in the hospital declined | A participatory approach in which the employees of a hospital, hospital cafeteria, and a residential home were integrated into the process of developing and implementing measures to counteract food waste |
Intervention Types
Drivers A-Knowledge H-Marketing practices I-Psychosocial factors |
Criteria not met: no control group; other limitations: unable to unpack individual effects of each intervention element |
| Setting: Retail Establishment | ||||
| Tier 2b | ||||
| Grewal et al., 2019 (Sweden) |
Shoppers exposed to a positive self-esteem ad were significantly more likely to choose unattractive apples than those exposed to the control ad: within each advertising condition, shoppers exposed to the control ad message chose attractive apples more often than unattractive apples; in contrast, for shoppers exposed to the positive self-esteem message condition, the choice of attractive and unattractive apples was split evenly |
2 × 2 intervention design:
|
Intervention Types
Drivers H-Marketing practices I-Psychosocial factors |
Criteria not met: wasted food not measured (self-report only); other limitations: only short-run effects assessed |
| Study | Findings | Intervention | Intervention Types and Driversa | Limitations |
|---|---|---|---|---|
| Young et al., 2017 (United Kingdom) |
Both the intervention and the control groups self-reported reductions in food waste; the use of social media did not change behavior as self-reported by consumers |
Two interventions with messages to encourage reductions in food waste from the standard “Love Food Hate Waste” campaign; interventions differed in the communication channel, not the message:
|
Intervention Types
Drivers A- Knowledge |
Criteria not met: wasted food not measured; other limitations: control group not randomly assigned |
| Young et al., 2018 (United Kingdom) |
Both treatment and control groups reported reduced food waste significantly |
Various interventions throughout 2 years with messages to encourage reductions in food waste from the standard “Love Food Hate Waste” campaign; interventions differed in the communication channel, not the message:
|
Intervention Types
Drivers A- Knowledge |
Criteria not met: wasted food not measured (self-report only); control group not randomly assigned |
| Study | Findings | Intervention | Intervention Types and Driversa | Limitations |
|---|---|---|---|---|
| van Giesen and de Hooge, 2019 (various countries) |
Sustainability and authenticity positioning can motivate consumers to purchase suboptimal products, independently of their prices; respondents exposed to authenticity positioning reported higher quality perceptions than respondents exposed to sustainability positioning | 3 × 3 × 2 intervention design in virtual retail store: three signs over suboptimal products: (1) sustainability-“Embrace imperfection:Join the fight against food waste!”; (2) authenticity- “Naturally imperfect: Apples the way they actually Look!”; (3) control with three prices (discount, moderate, discount, same price) and with two products (apples and carrots) |
Intervention Types
Drivers H-Marketing practices I-Psychosocial factors |
Criteria not met: wasted food not measured; other limitations: sample was biased as individuals were already caring for the environment; experiment was conducted in virtual retail store |
| Aschemann-Witzel, 2018 (Denmark) |
Message on the sticker appealing to either a food waste avoidance or to a cost-saving motive did not significantly influence likelihood of choice; however, familiarity and perceived quality was important in whether suboptimal food would be purchased | 2 × 4 intervention design in virtual retail store: two product qualities (optimal product vs. suboptimal product) and four messages on stickers: (1) priced reduced; (2) fight food waste; (3) reduced item: lower price and save more; and (4) fight food waste and ‘lower price—save more |
Intervention Types
Drivers H-Marketing practices I-Psychological factors |
Criteria not met: food waste not measured; other limitations: experiment was conducted in virtual retail store |
| Aschemann-Witzel et al., 2018 (Uruguay) |
Communicating the budget saving did not increase choice likelihood of suboptimal product, but communicating the food waste avoidance increases choice likelihood, independent of the product type; when no messages were displayed, there were significant food category differences in choice likelihood | 2 × 2 intervention design in virtual retail store (with four food products): two product qualities (optimal product vs. suboptimal product) and two messages on stickers (Offer! Super saver! or Choose this product and help to reduce food waste). |
Intervention Types
Drivers H-Marketing practices I-Psychological factors |
Criteria not met: food waste not measured; other limitations: experiment was conducted in virtual retail store |
| Kawata and Kubota, 2018 (Japan) |
Willingness to pay for reprocessed domestic and foreign Kara-age was 92.8 percent and 91.7 percent of the prices of regular Kara-age, respectively, showing the feasibility of selling reprocessed form of the product and reducing food waste in the supply chain | Surveys on willingness to pay for three choices: (1) regular Kara-age (i.e., made from fresh raw chicken), (2) reprocessed Kara-age (i.e., made from unsold raw chicken near its sell-by date), and (3) no buy |
Intervention Types
Drivers H-Marketing practices I-Psychological factors |
Criteria not met: food waste not measured; other limitations: experiment was conducted as a survey |
| Study | Findings | Intervention | Intervention Types and Driversa | Limitations |
|---|---|---|---|---|
| Del Giudice et al., 2016 (Italy) |
The effect of certification on participants’ willingness to pay was significant; the importance of providing footprint information was only observed for the baguette from the retailer with 1 percent food waste certified |
An experimental auction to measure willingness to pay for the following choices:
|
Intervention Types
Drivers H-Marketing practices I-Psychosocial factors |
Criteria not met: food waste not measured; other limitations: related to the experiment being conducted as an auction; population was from undergraduate students who might be more aware than others of environmental effects of food waste |
| Collart and Interis, 2018 (United States) |
Clarifying the meaning of date labels was insufficient to change preferences for food past its best-before date; when information about the environmental implications of food waste was provided, participants’ willingness-to-pay for expired food increased, particularly for expired frozen or recently expired semi-perishable products |
Participants were asked to choose between food products of varying perishability level at various dates before or after their best-before dates. Interventions:
|
Intervention Types
Drivers B-Assessing risk I-Psychosocial factors K-Policy |
Criteria not met: food waste not measured |
| Le Borgne et al., 2018 (France) |
Consumers’ perceived probability of waste had a significant negative effect on consumers’ attitude toward promotions and consumers’ intention to choose perishable food products (cheese and bread) on sale; participants showed skepticism toward the “Buy Two Get One Free Later” offer. | Hypothetical product purchase setting to assess the impact of multiproduct sales tactics on intended food waste |
Intervention Types
Drivers G-Everyday complexity H-Marketing practices |
Criteria not met: food waste not measured; other limitations: statistical testing for intention to discard not clearly communicated; effects unclear |
| Study | Findings | Intervention | Intervention Types and Driversa | Limitations |
|---|---|---|---|---|
| Petit et al., 2019 (United States |
Study 1 found that package size affects the anticipated food waste among consumers and that anticipating food waste mediated purchasing intentions, but only observed for perishable products; Study 2 found that priming individuals with information about the consequences of food waste made them more likely to focus on their anticipated food waste and thereby reduce their preference for bonus packs; Study 3 found that anticipated food waste decreased when small packages were sold partitioned, while it increases when large packages were sold partitioned |
Consumer survey asking about hypothetical product purchases; four different studies:
|
Intervention Types
Drivers F-Dietary differences G-Everyday complexity H-Marketing practices |
Criteria not met: food waste not measured |
|
| Study | Findings | Intervention | Intervention Types and Driversa | Limitations |
|---|---|---|---|---|
| Setting: Household | ||||
| Tier 1b | ||||
| Ilyuk, 2018. Study 3 (United States) |
Waste likelihood was higher when consumers purchased food items online than when they purchased them in a store |
Participants were randomly assigned to one of three conditions:
|
Intervention Types
Drivers E-Psychological distance |
Criteria not met: studies 1 and 2 did not measure actual waste; study 3 measured actual waste, but report did not quantify reduction and relied on university students |
| Romani et al., 2018 (Italy) |
Information intervention led to a significant effect size in reducing food waste | Longitudinal study with information intervention to illustrate how to organize a weekly menu quickly and simply and a printable Excel file designed to support meal organization and preparation |
Intervention Types
Drivers A-Knowledge |
Reliance on household food waste diaries, which are known for underreporting, and sample attrition of about 10 percent between baseline and post-treatment measurement |
| van der Werf et al., 2019 (Canada) |
Intervention led to a significant reduction in total food waste. | Multifaceted informational intervention in the form of a package designed to extend produce life with the following elements: (1) environmental and social impacts of wasted food; (2) local averages of amount and value of wasted food; (3) reduction tips, such as food planning and use of leftovers; (4) five emails sent over the course of 2 weeks to reinforce the messages |
Intervention Types
Drivers A-Knowledge D-Lack of awareness/monitoring E-Psychological distance |
Unable to unpack individual effects of each intervention element |
| Soma et al., 2020 (Canada) |
The passive group and the gamification group had higher self-reported awareness of food wasting and lower food wastage than the control group; waste audits found marginally significant differences between the gamification group and the control group and no difference between the other campaign groups and the control group in edible food wasted; frequent gamers were found to generate less edible food waste than infrequent gamers |
Three different interventions:
|
Intervention Types
Drivers A-Knowledge |
| Study | Findings | Intervention | Intervention Types and Driversa | Limitations |
|---|---|---|---|---|
| Lee and Jung, 2017 (South Korea) |
The Household-Based Food Waste Charging System can reduce more food waste than the design where all residents pay the same amount for the waste | Household-Based Food Waste Charging System, which uses a weight based payment design, through which each household is electronically charged for the weight of food waste they disposes of Control: residents pay the same price by dividing total amount of waste charge by number of households |
Intervention Types
Drivers D-Lack of awareness/monitoring K-Policy |
Natural experiment; no randomization of households |
| Tier 2b | ||||
| David et al., 2019 (Australia) |
Two behavioral states were identified: fruit and vegetable wasters and nonwasters; following the intervention, a significant percentage of people transitioned away from wasters to nonwasters | Multifaceted intervention (2 weeks) included providing a shopping bag, chopping board, 16 new leftover reuse recipe cards, invitation flyer and a shopping list and in-store cooking demonstrations |
Intervention Types
Drivers A-Knowledge |
Criteria not met: wasted food not measured; no control group; other limitations: only fruit and vegetable waste was considered; unable to unpack individual effects of each intervention element |
| Devaney and Davies, 2017 (Ireland) |
Participant households reduced their overall food waste generation | Multifaceted intervention directed at changing behaviors toward being more sustainable, through purchasing, storage, and preparation, including information (a guide to smarter food storage), tools (compostable food waste boxes) |
Intervention Types
Drivers A-Knowledge |
Criteria not met: no control group; other limitations: unable to unpack individual effects of each intervention element; only fruit and vegetable waste considered; N = 5. |
| Dyen and Sirieix, 2016 (France) |
Cooking classes were efficient to promote less food waste | Ongoing cooking classes on how to cook with products from the food bank in social center for people with social instability |
Intervention Types
Drivers A-Knowledge |
Criteria not met: no control group; statistical significance not assessed; other limitations: N = 3 |
| Study | Findings | Intervention | Intervention Types and Driversa | Limitations |
|---|---|---|---|---|
| Graham-Rowe et al., 2019 (United Kingdom) |
There was a higher difference in fruit and vegetable waste before and after reading information about the negative consequences of household food waste for the standard self-affirmation group than for the control group |
Online questionnaire where participants read information about the negative consequences of household food waste after:
|
Intervention Types
Drivers H-Marketing practices I-Psychosocial factors |
Criteria not met: wasted food not measured; other limitations: only fruit and vegetable waste considered |
| Gutiérrez-Barba and Ortega-Rubio, 2013 (Mexico) |
There was reduction in food waste among families in the intervention group; food waste reduction was not reported for control group | Eight families attending a 32-hour workshop on the health and environmental impacts and skills and technical expertise to reducing food waste in six weekly sessions; the control group was 33 families not attending the workshop |
Intervention Types
Drivers A-Knowledge I-Psychosocial factors |
Criteria not met: inadequate statistical reporting; other limitations: control group not randomly assigned |
| Kowalewska and Kołłajtis-Dołowy, 2018 (Poland) |
Questionnaires showed that knowledge about food waste increased after the intervention; analyses of the effect of intervention on food waste was not conducted | Intervention via four short (3- to 4-minute) education videos on food wastage and its prevention |
Intervention Types
Drivers A-Knowledge I-Psychosocial factors |
Criteria not met: wasted food not measured (self-report only); inadequate statistical reporting |
| Lim et al., 2017 (The Netherlands) |
Intervention raised awareness; behavior change was not explored nor claimed in this study |
Technology intervention:
|
Intervention Types
Drivers A-Knowledge D-Lack of awareness/monitoring I-Psychosocial factors J-Built environment |
Criteria not met: waste food was not measured (self-report only); inadequate statistical reporting |
| Morone et al., 2018 (Italy) |
The adoption of food sharing practices by households did not automatically translate into food waste reduction | Intervention: students were instructed to purchase, cook, and consume food collectively |
Intervention Types
Drivers A-Knowledge I-Psychosocial factors |
Evident problems included participants who dropped out or cheated |
| Study | Findings | Intervention | Intervention Types and Driversa | Limitations |
|---|---|---|---|---|
| Rohm et al., 2017 (Denmark, Germany, Norway, Sweden and The Netherlands) |
Brochures and refrigerator magnets had no detectable effect on consumer attitudes, self-reported behavior, and suboptimal food choice; more consumers bought a banana when the sustainability message was next to them than when the price was lowered or when a taste message was presented |
Multifaceted intervention was used to motivate consumers to purchase and accept suboptimal food in stores and in their households:
An in-store intervention where different messages were tested to identify the potential effects on consumer behavior |
Intervention Types
Drivers H-Marketing practices I-Psychosocial factors |
|
| Schmidt, 2016 (Germany) |
Significant higher increase in the self-reported performance of recorded food waste-preventing behaviors in the experimental group than in the control group |
Multifaceted interventions (4 weeks):
|
Intervention Types
Drivers A-Knowledge I-Psychosocial factors |
Criteria not met: wasted food not measured (self-report only); other limitations: unable to unpack individual effects of each intervention element |
| Comber and Thieme, 2013 (United Kingdom) |
The intervention had no effect on changes in attitude toward recycling and food waste but had an impact on participants’ awareness of their own and others’ recycling behavior and this awareness prompted self-reflection and reevaluation of the facilities and abilities available to the participants for recycling | Multifaceted intervention: a two-part persuasive technology, which replaced an everyday waste bin with one enabled to capture and share images of disposed of waste on an online social network |
Intervention Types
Drivers D-Lack of awareness-monitoring I-Psychosocial factors |
Criteria not met: no control group; no measure of food waste, but only measure of attitude and behavior related to recycling and food waste |
| Sintov et al., 2017 (United States) |
No evidence for positive spillover effects on energy and water behaviors but none of the three food spillover behaviors were significant (food, energy, and water waste prevention), except for a marginal effect for checking food before shopping | Individuals received curbside organic waste bins (structural intervention) and procedural information about composting (information intervention) were randomly assigned, following the midpoint assessment, to receive weekly descriptive norms messaging for 8 weeks: 75 percent of households in Costa Mesa separated all of their food scraps this week, |
Intervention Types
Drivers C-Waste vs. other goals D-Lack of awareness/monitoring G-Everyday complexity I-Psychosocial factors J-Built environment |
Criteria not met: food waste not measured, but only measure of food waste prevention behaviors (planning out meals and assessing/using food at home before shopping) were recorded by survey |
| Study | Findings | Intervention | Intervention Types and Driversa | Limitations |
|---|---|---|---|---|
| Roe et al., 2018 (United States) |
Containers with date labels resulted in increases in discard intentions for milk that is putatively -Past Date- among commercial bottlers compared with containers without such labels; multivariate analysis revealed that discard intentions are lower among participants with higher incomes and fewer household members, but revealed no other significant correlations with personal or household characteristics | A “sell by” label with a date set to 18 days post-bottling |
Intervention Types
Drivers B-Assessing risk I-Psychosocial factors K-Policy |
Criteria not met: food waste was not measured |
| Wilson et al., 2017 (United States) |
The willingness to waste was greatest in the -Use By- treatment, the date label which may be the least ambiguous and suggestive of food safety; the willingness to waste was the lowest for the “Sell by: treatment, which may be the most ambiguous date label about safety or quality for consumers | Labels on “Best by”, “Fresh by”, “Use by” or “Sell by” on three products, two sizes, and three expiration dates |
Intervention Types
Drivers B-Assessing risk I-Psychosocial factors K-Policy |
Criteria not met: food waste not measured |
| Manzocco et al., 2017 (Italy) |
Increase in storage temperature did not affect salad firmness and weight loss but increased color changes, microbial growth, and consumer rejection; the survey showed that fresh-cut salad was mainly consumed within the first 5 days after purchasing | A survey on salad consumption: participants were asked to consider discard of the product, which was presented to them after being held at different refrigeration temperatures, without their knowledge; participants were also asked to report on their usual habits regarding acquisition and shelf life of lettuce in their households |
Intervention Types
Drivers J-Built environment |
Criteria not met: food waste not measured; other limitations: effects unclear; estimated a rejection curve but did not provide straightforward tests of the effect of temperature on intended discard rate |
NOTES: EPA, U.S. Environmental Protection Agency; FAO, U.N. Food and Agriculture Organization; USDA, U.S. Department of Agriculture.
a Drivers are defined as: A-knowledge; B-assessing risk; C-waste vs. other goals; D-lack of awareness/monitoring; E-psychological distance; F-dietary differences; G-everyday complexity; H-marketing practices; I-psychosocial factors; J-built environment; and K-policy.
b Tier 1 studies met four criteria: an intervention was implemented; wasted food was measured; causal effect can be attributed; and statistical analysis was adequate. Tier 2 studies failed to meet at least one of the four criteria.
REFERENCES
Ahmed, S., C.B. Shanks, M. Lewis, A. Leitch, C. Spencer, E.M. Smith, and D. Hess. 2018. Meeting the food waste challenge in higher education. International Journal of Sustainability in Higher Education 19(6):1075-1094.
ARAMARK. 2008. The Business and Cultural Acceptance Case for Trayless Dining. Available: http://www.elon.edu/docs/e-web/bft/sustainability/ARAMARK%20Trayless%20Dining%20July%202008%20FINAL.pdf.
Aschemann-Witzel, J. 2018. Consumer perception and preference for suboptimal food under the emerging practice of expiration date based pricing in supermarkets. Food Quality and Preference 63(63):119-128.
Aschemann-Witzel, J., A. Giménez, and G. Ares. 2018. Consumer in-store choice of suboptimal food to avoid food waste: The role of food category, communication and perception of quality dimensions. Food Quality and Preference 68:29-39.
Barnes, J.L., and C.A. Warren. 2017. Development of food group books for three- and four-year-old children. Family and Consumer Sciences Research Journal 45(3):272-284.
Bel, G., and R. Gradus. 2016. Effects of unit-based pricing on household waste collection demand: A meta-regression analysis. Resource and Energy Economics 44:169-182.
Belavina, E., K. Girotra, and A. Kabra. 2017. Online grocery retail: Revenue models and environmental impact. Management Science 63(6):1781-1799.
Bergman, E.A., N.S. Buergel, T.F. Englund, and A. Femrite. 2004. The relationship of meal and recess schedules to plate waste in elementary schools. The Journal of Child Nutrition and Management 28(2).
Berkowitz, S., L. Marquart, E. Mykerezi, D. Degeneffe, and M. Reicks. 2016. Reduced-portion entrées in a worksite and restaurant setting: Impact on food consumption and waste. Public Health Nutrition 19(16):3048-3054.
Cardwell, N.T., C. Cummings, and M. Kraft. 2019. Toward Cleaner Plates: A study of plate waste in food service. Available: https://www.nrdc.org/resources/toward-cleanerplates-study-plate-waste-food-service.
Chan, H.W., T.B. Champoux, M. Reicks, Z. Vickers, and L. Marquart. 2008. White wholewheat flour can be partially substituted for refined-wheat flour in pizza crust in school meals without affecting consumption. Journal of Child Nutrition & Management: A publication of the American School Food Service Association 32:1.
Chen, H.S., and T.M.C. Jai. 2018. Waste less, enjoy more: Forming a messaging campaign and reducing food waste in restaurants. Journal of Quality Assurance in Hospitality and Tourism 19(4):495-520.
Chitnis, M., D. Angela, S. Steve, K.F. Steven, and J. Tim. 2014. Who rebounds most? Estimating direct and indirect rebound effects for different UK socioeconomic groups. Ecological Economics 106(106):12-32.
Cohen, J.F.W., L.A. Smit, E. Parker, S.B. Austin, A.L. Frazier, C.D. Economos, and E.B. Rimm. 2012. Long-term impact of a chef on school lunch consumption: Findings from a 2-year pilot study in Boston middle schools. Journal of the Academy of Nutrition and Dietetics 112(6):927-933.
Cohen, J.F., S. Richardson, E. Parker, P.J. Catalano, and E.B. Rimm. 2014. Impact of the new U.S. Department of Agriculture school meal standards on food selection, consumption, and waste. American Journal of Preventive Medicine 46(4):388-394.
Collart, A.J., and M.G. Interis. 2018. Consumer imperfect information in the market for expired and nearly expired foods and implications for reducing food waste. Sustainability (Switzerland) 10(11).
Comber, R., and A. Thieme. 2013. Designing beyond habit: Opening space for improved recycling and food waste behaviors through processes of persuasion, social influence and aversive affect. Personal and Ubiquitous Computing 17(6):1197-1210.
David, P., S. Rundle-Thiele, and J.I. Pallant. 2019. Re(focussing) on behavioural change: An examination of the utility of hidden Markov modelling. Journal of Social Marketing 9(2):130-145.
Del Giudice, T., F. La Barbera, R. Vecchio, and F. Verneau. 2016. Anti-waste labeling and consumer willingness to pay. Journal of International Food & Agribusiness Marketing 28(2):149-163.
Devaney, L., and A.R. Davies. 2017. Disrupting household food consumption through experimental homelabs: Outcomes, connections, contexts. Journal of Consumer Culture 17(3):823-844.
Duret, S., H.M. Hoang, E. Derens-Bertheau, A. Delahaye, O. Laguerre, and L. Guillier. 2019. Combining quantitative risk assessment of human health, food waste, and energy consumption: The next step in the development of the food cold chain? Risk Analysis: An Official Publication of the Society for Risk Analysis 39(4):906-925.
Dyen, M., and L. Sirieix. 2016. How does a local initiative contribute to social inclusion and promote sustainable food practices? Focus on the example of social cooking workshops. International Journal of Consumer Studies 40(6):685-694.
Ellison, B., O. Savchenko, C.J. Nikolaus, and B.R.L. Duff. 2019. Every plate counts: Evaluation of a food waste reduction campaign in a university dining hall. Resources, Conservation and Recycling 144:276-284.
Freedman, M.R., and C. Brochado. 2010. Reducing portion size reduces food intake and plate waste. Obesity 18(9):1864-1866.
Gee, I.M., F.T. Davidson, B.L. Speetles, and M.E. Webber. 2019. Deliver me from food waste: Model framework for comparing the energy use of meal-kit delivery and groceries. Journal of Cleaner Production 236:117587.
Graham-Rowe, E., D.C. Jessop, and P. Sparks. 2019. Self-affirmation theory and pro-environmental behaviour: Promoting a reduction in household food waste. Journal of Environmental Psychology 62:124-132.
Grewal, L., J. Hmurovic, C. Lamberton, and R.W. Reczek. 2019. The self-perception connection: Why consumers devalue unattractive produce. Journal of Marketing 83(1):89-107.
Gutiérrez-Barba, B.E., and A. Ortega-Rubio. 2013. Household food-waste production and a proposal for its minimization in Mexico. Life Science Journal 10(3):1772-1783.
Hamerman, E.J., F. Rudell, and C.M. Martins. 2018. Factors that predict taking restaurant leftovers: Strategies for reducing food waste. Journal of Consumer Behaviour 17(1):94-104.
Hamilton, S.F., and T.J. Richards. 2019. Food policy and household food waste. American Journal of Agricultural Economics 101(2):600-614.
Heard, B.R., M. Bandekar, B. Vassar, and S.A. Miller. 2019. Comparison of life cycle environmental impacts from meal kits and grocery store meals. Resources, Conservation and Recycling 147:189-200.
Ilyuk, V. 2018. Like throwing a piece of me away: How online and in-store grocery purchase channels affect consumers’ food waste. Journal of Retailing and Consumer Services 41:20-30.
Jagau, H.L., and J. Vyrastekova. 2017. Behavioral approach to food waste: An experiment. British Food Journal 119(4):882-894.
Kallbekken, S., and H. Sælen. 2013. ‘Nudging’ hotel guests to reduce food waste as a win–win environmental measure. Economics Letters 119(3):325-327.
Kandemir, C., T. Quested, C. Reynolds, K. Fisher, and R. Devine. 2019. Household food waste simulation model: Investigation of interventions for staple food items waste. Paper read at Proceedings of 26th EurOMA Conference.
Kawata, Y., and S. Kubota. 2018. Consumers’ willingness to pay for reprocessed fried chicken: A way of reducing uneaten food. Appetite 120:571-577.
Khan, U., and R. Dhar. 2006. Licensing effect in consumer choice. Journal of Marketing Research 43(2):259-266.
Kim, K., and S. Morawski. 2013. Quantifying the impact of going trayless in a university dining hall. Journal of Hunger and Environmental Nutrition 7(4):482-486.
Kowalewska, M.T., and A. Kołłajtis-Dołowy. 2018. Food, nutrient, and energy waste among school students. British Food Journal 120(8):1807-1831.
Kuo, C., and Y. Shih. 2016. Gender differences in the effects of education and coercion on reducing buffet plate waste. Journal of Foodservice Business Research 19(3):223-235.
Kuperberg, K., A. Caruso, S. Dello, and D. Mager. 2008. How will a room service delivery system affect dietary intake, food costs, food waste and patient satisfaction in a paediatric hospital? A pilot study. Journal of Foodservice 19(5):255-261.
Lazell, J. 2016. Consumer food waste behaviour in universities: Sharing as a means of prevention. Journal of Consumer Behaviour 15(5):430-439.
Le Borgne, G., L. Sirieix, and S. Costa. 2018. Perceived probability of food waste: Influence on consumer attitudes towards and choice of sales promotions. Journal of Retailing and Consumer Services 42:11-21.
Lee, S. and K. Jung. 2017. Exploring effective incentive design to reduce food waste: A natural experiment of policy change from community based charge to RFID based weight charge. Sustainability 9:1-17.
Lim, V., M. Funk, L. Marcenaro, C. Regazzoni, and M. Rauterberg. 2017. Designing for action: An evaluation of social recipes in reducing food waste. International Journal of Human-Computer Studies 100:18-32.
Liz Martins, M., S.S. Rodrigues, L.M. Cunha, and A. Rocha. 2016. Strategies to reduce plate waste in primary schools—experimental evaluation. Public Health Nutrition 19(8):1517-1525.
Lorenz-Walther, B.A., N. Langen, C. Gobel, T. Engelmann, K. Bienge, M. Speck, and P. Teitscheid. 2019. What makes people leave less food? Testing effects of smaller portions and information in a behavioral model. Appetite 139:127-144.
Manomaivibool, P., C. Chart-asa, and P. Unroj. 2016. Measuring the impacts of a save food campaign to reduce food waste on campus in Thailand. Applied Environmental Research 38:13-22.
Manzocco, L., C. Lagazio, M. Alongi, M.C. Nicoli, and S. Sillani. 2017. Effect of temperature in domestic refrigerators on fresh-cut iceberg salad quality and waste. Food Research International 102(102):129-135.
Morone, P., M. Andrea, I. Enrica, and F. Pasquale Marcello. 2018. Does food sharing lead to food waste reduction? An experimental analysis to assess challenges and opportunities of a new consumption model. Journal of Cleaner Production 185(185):749-760.
Petit, O., R. Lunardo, and B. Rickard. 2019. Small is beautiful: The role of anticipated food waste in consumers’ avoidance of large packages. Journal of Business Research 113:326-336.
Pinto, R.S., R. Pinto, F.F.S. Melo, S.S. Campos, and C.M. Cordovil. 2018. A simple awareness campaign to promote food waste reduction in a university canteen. Waste Management 76:28-38.
Prescott, M.P., X. Burg, J.J. Metcalfe, A.E. Lipka, C. Herritt, and L. Cunningham-Sabo. 2019. Healthy planet, healthy youth: A food systems education and promotion intervention to improve adolescent diet quality and reduce food waste. Nutrients 11(8).
Qi, D., and B.E. Roe. 2017. Foodservice composting crowds out consumer food waste reduction behavior in a dining experiment. American Journal of Agricultural Economics 99(5):1159-1171.
Roe, B.E., S.B. Aishwarya, T.S. Christopher, M.P. David, R.H. Dennis, and E.B. Kathryn. 2018. Discard intentions are lower for milk presented in containers without date labels. Food Quality and Preference 66(66):13-18.
Rohm, H., M. Oostindjer, J. Aschemann-Witzel, C. Symmank, V.L. Almli, I.E. de Hooge, A. Normann, and K. Karantininis. 2017. Consumers in a sustainable food supply chain (COSUS): Understanding consumer behavior to encourage food waste reduction. Foods 6(12):27.
Romani, S., S. Grappi, R.P. Bagozzi, and A.M. Barone. 2018. Domestic food practices: A study of food management behaviors and the role of food preparation planning in reducing waste. Appetite 121:215-227.
Sandson, K., and E. Broad Lieb. 2019. Bans and Beyond: Designing and Implementing Organic Waste Bans and Mandatory Organics Recycling Laws. Harvard Law School Food Law and Policy Clinic; Center for EcoTechnology. Available: https://www.chlpi.org/wp-content/uploads/2013/12/Organic-Waste-Bans_FINAL-compressed.pdf.
Sarjahani, A., E.L. Serrano, and R. Johnson. 2009. Food and non-edible, compostable waste in a university dining facility. Journal of Hunger and Environmental Nutrition 4(1):95-102.
Schmidt, K. 2016. Explaining and promoting household food waste-prevention by an environmental psychological based intervention study. Resources, conservation, and recycling 111(111):53-66.
Schwartz, M.B., K.E. Henderson, M. Read, N. Danna, and J.R. Ickovics. 2015. New school meal regulations increase fruit consumption and do not increase total plate waste. Childhood Obesity 11(3):242-247.
Sintov, N., S. Geislar, and L.V. White. 2017. Cognitive accessibility as a new factor in proenvironmental spillover: Results from a field study of household food waste management. Environment and Behavior 51(1):50-80.
Soma, T., B. Li, and V. Maclaren. 2020. Food waste reduction: A test of three consumer awareness interventions. Sustainability (Switzerland) 12(3):907.
Somkun, P.N. 2017. Stochastic mathematical model for food waste reduction in a two-level supply chain for highly perishable products. International Journal of Supply Chain Management 6(1):165-171.
Stockli, S., M. Dorn, and S. Liechti. 2018. Normative prompts reduce consumer food waste in restaurants. Waste Management 77:532-536.
Strotmann, C., S. Friedrich, J. Kreyenschmidt, P. Teitscheid, and G. Ritter. 2017. Comparing food provided and wasted before and after implementing measures against food waste in three healthcare food service facilities. Sustainability (Switzerland) 9(8).
Thiagarajah, K., and V.M. Getty. 2013. Impact on plate waste of switching from a tray to a trayless delivery system in a university dining hall and employee response to the switch. Journal of the Academy of Nutrition and Dietetics 113(1):141-145.
van der Werf, P., J.A. Seabrook, and J.A. Gilliland. 2019. “Reduce food waste, save money”: Testing a novel intervention to reduce household food waste. Environment and Behavior. Available: https://doi.org/10.1177/0(0):0013916519875180.
van Giesen, R.I., and I.E. de Hooge. 2019. Too ugly, but I love its shape: Reducing food waste of suboptimal products with authenticity (and sustainability) positioning. Food Quality and Preference 75:249-259.
van Holsteijn, F., and R. Kemna. 2018. Minimizing food waste by improving storage conditions in household refrigeration. Resources, Conservation & Recycling 128(128):25-31.
Vermote, M., V. Versele, M. Stok, P. Mullie, E. D’Hondt, B. Deforche, P. Clarys, and T. Deliens. 2018. The effect of a portion size intervention on French fries consumption, plate waste, satiety and compensatory caloric intake: An on-campus restaurant experiment. Nutrition Journal 17(1).
Wansink, B., and K. van Ittersum. 2013. Portion size me: Plate-size induced consumption norms and win-win solutions for reducing food intake and waste. Journal of Experimental Psychology: Applied 19(4):320-332.
Whitehair, K.J., C.W. Shanklin, and L.A. Brannon. 2013. Written messages improve edible food waste behaviors in a university dining facility. Journal of the Academy of Nutrition and Dietetics 113(1):63-69.
Wikström, F., H. Williams, and G. Venkatesh. 2016. The influence of packaging attributes on recycling and food waste behaviour—an environmental comparison of two packaging alternatives. Journal of Cleaner Production 137:895-902.
Williamson, S., L.G. Block, and P.A. Keller. 2016. Of waste and waists: The effect of plate material on food consumption and waste. Journal of the Association for Consumer Research 1(1):147-160.
Wilson, N.L.W., B.J. Rickard, R. Saputo, and S.-T. Ho. 2017. Food waste: The role of date labels, package size, and product category. Food Quality and Preference 55:35-44.
WRAP. 2013. The Milk Model: Simulating Food Waste in the Home. Available: https://www.wrap.org.uk/sites/files/wrap/Milk%20Model%20report.pdf.
Young, W., S.V. Russell, C.A. Robinson, and R. Barkemeyer. 2017. Can social media be a tool for reducing consumers’ food waste? A behaviour change experiment by a UK retailer. Resources, Conservation and Recycling 117:195-203.
Young, C.W., S.V. Russell, C.A. Robinson, and P.K. Chintakayala. 2018. Sustainable retailing—influencing consumer behaviour on food waste. Business Strategy and the Environment 27(1):1-15.
Yu, Y., and E.C. Jaenicke. (2020). The effect of sell-by dates on purchase volume and food waste. Food Policy. Available: https://doi.org/10.1016/j.foodpol.2020.101879.
