Research published in BMCPublicHealth found that a 30% price increase for meat decreased meat purchases in a virtual supermarket when combined with an information nudge on the environmental impact of meat production and consumers' role in that regard.
Background Reduced meat consumption benefits human and planetary health. Modelling studies have demonstrated the significant health and environmental gains that could be achieved through fiscal measure s targeting meat. Adding other interventions may enhance the effect of a fiscal measure . The current study aimed to examine the effect of higher meat prices, an information nudge and a combination of both measures on meat purchases in a three-dimensional virtual supermarket. Methods A parallel designed randomised controlled trial with four conditions was performed. Participants (≥ 18 years) were randomly assigned to the control condition or one of the experimental conditions: a 30% price increase for meat (‘Price condition’), an information nudge about the environmental impact of meat production and consumers’ role in that regard (‘Information nudge condition’) or a combination of both (‘Combination condition’). Participants were asked to shop for their household for one week. The primary outcome was the difference in the total amount of meat purchased in grams per household per week. Results Between 22 June 2020 and 28 August 2020, participants were recruited and randomly assigned to the control and experimental conditions. The final sample included 533 participants. In the ‘Combination condition’, − 386 g (95% CI: − 579, − 193) meat was purchased compared with the ‘Control condition’. Compared to the ‘Control condition’ less meat was purchased in the ‘Price condition’ (− 144 g (95%CI: − 331, 43)), although not statistically significant, whereas a similar amount of meat was purchased in the ‘Information nudge condition’ (1 g (95%CI: − 188, 189)). Conclusion Achieving the most pronounced effects on reduced meat purchases will require a policy mixture of pricing and an information nudge. Less meat is purchased in a virtual supermarket after raising the meat price by 30% combined with an information nudge. The results could be used to design evidence-based policy measure
3/year). GHG emissions is used as a proxy for other indicators as GHG emissions highly correlates with other environmental indicators. Blue water consumption and land use had the weakest correlation with GHG emissions in the Dutch LCA Food database and are therefore also included in this study [
24].Characteristics of the population and secondary outcomes were summarised with descriptive statistics in means and standard deviations (SD) or median and interquartile range (IQR) for continuous variables, and in numbers and percentages for categorical variables. Outcomes were visually inspected for normality using Q,Q-plots and Kolmogorov -Smirnov tests. The primary measure total amount of meat purchases followed a normal distribution. Linear regression models with the total amount of meat purchases as a dependent variable and the conditions as independent variables were used to examine the potential effect modifier education level, as individuals with a lower socio-economic position might respond differently upon the interventions [
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Indicators included were greenhouse gas (GHG) emissions (in kg CO 2 -equivalents), blue water consumption (water sources from surface or groundwater resources) (in m 3 ) and land use (in m 2 /year). GHG emissions is used as a proxy for other indicators as GHG emissions highly correlates with other environmental indicators.30pm and 6. Blue water consumption and land use had the weakest correlation with GHG emissions in the Dutch LCA Food database and are therefore also included in this study [ 27 ]. The LCA’s were linked via NEVO codes. Octopus said the average household saved 23p per two-hour period, though some saved as much as £4. For which no primary LCA data were available the LCA’s were linked to similar foods based on similarities in types of food, production systems and ingredient composition. Better yet, you can save a further 15% off if you subscribe to repeat deliveries up to every 6 months, bagging it for £62.
For composite dishes, standardized recipes from the Dutch food composition database (NEVO-online version 2016/5. A spokeswoman for National Grid ESO told The Times: "Demand shifting has the potential to save consumers money, reduce carbon emissions and offer greater flexibility on the system.0) were used where available and if not available, recipes were based on label information [ 24 ]. The Dutch food composition database (NEVO online version 2019/6. Read more: Those with smart meters would be able to take part in the scheme.0) was used to determine nutritional composition (via energy content (kcal), carbohydrates (in energy percentage (En%)), mono and disaccharides (in En%), fatty acids (in En%), saturated fatty acids (SFA) (in En%), protein (in En%), fibre (in En%) and salt (in g)) [ 24 ]. A final questionnaire was specified for the control and experimental conditions. Ofgem CEO Jonathan Brearley told LBC's Nick Ferrari: "The market is changing." Benefits We checked out the benefits of this super moisturising, yet ultra-light cream and they are impressive.
First, a set of general questions was provided, covering participants’ sex, height and weight, educational level, income, living situation, and, for instance, frequency of meat consumption. Depending on the experimental conditions, the questionnaire contained (three or six) additional questions. That's not a fixed number and it may well change before we get to October. Participants in the Information nudge condition and Price condition were asked three additional questions as to whether the notification (on the information nudge or price increase, respectively) before entering the virtual supermarket had been read (Yes or No), understood (Yes or No) and whether it had influenced the shopping behaviour (7-point Likert scale: 1 “not at all” to 7 “extremely”). In the Combination condition, the questions on the information nudge as well as on the higher meat prices were asked. "It could go up, it could go down", the Ofgem chief declared. Finally, the questionnaire covered questions about participants’ understanding of the software (5-point Likert scale: 1 “strongly disagree” to 5 “strongly agree”), whether the participants’ virtual supermarket groceries corresponded with their usual groceries (5-point Likert scale: 1 “strongly disagree” to 5 “strongly agree”), and whether the participants’ shopping budget was more, the same or less than usual. I feel like I’ve wasted a lot of money in the past.
Also, participants’ attention to the prices and the influence of pricing on their purchases were measured (7-point Likert scale: 1 “not at all” to 7 “extremely”). Experts at Cornwall Insights predict the price cap will rise to a higher £2,980 in October, before hitting £3,000 in January. Statistical analysis Characteristics of the population and secondary outcomes were summarised with descriptive statistics in means and standard deviations (SD) or median and interquartile range (IQR) for continuous variables, and in numbers and percentages for categorical variables. Outcomes were visually inspected for normality using Q,Q-plots and Kolmogorov -Smirnov tests. The primary measure total amount of meat purchases followed a normal distribution. Linear regression models with the total amount of meat purchases as a dependent variable and the conditions as independent variables were used to examine the potential effect modifier education level, as individuals with a lower socio-economic position might respond differently upon the interventions [ ]. Stacey revealed on Instagram that she loves to keep her cream in her beauty fridge after finding that she loves it"when it's gone freezing cold and you put it on your skin.
The variable educational level was added to the unadjusted model with interaction terms between the variable and the intervention conditions to examine effect modification. Interaction terms were not statistically significant ( p > 0·05) and therefore removed from the model. In the first model, the variable household size was added to the model since this variable is a strong predictor for the total amount of (meat) purchases (model 1). Certain imbalances in characteristics were observed between the conditions, although the drop-out across study conditions was similar. In the second model, further adjustments were therefore made for sex, BMI and educational level to correct for imbalances between the conditions (model 2). To find out more visit our FAQ page.
Parameter estimates were obtained using generalised linear models and included regression coefficients (β) (representing the absolute mean difference in meat purchases (in g per household per week) for the experimental conditions relative to the control condition (reference) and 95% confidence interval (95%CI) of the mean difference. A sensitivity analysis was performed, in which participants in the experimental conditions were excluded who did not read or understand the notifications before entering the supermarket. Furthermore, in a second sensitivity analysis participants were excluded who defined themselves as vegan, vegetarian or pescatarian. Participants were excluded for analysis if fewer than or equal to five different products were purchased since this type of grocery shopping is not representative of a typical weekly shop. The statistical analysis was performed using SAS software, version 9.
4 (SAS Institute Inc., Cary, NC, USA). A two-sided p -value of < 0·05 was considered statistically significant. Role of the funding source The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Results From 22 June 2020 to 28 August 2020, 150,514 panel members were invited to participate in the study.
Of these, 12,901 individuals completed the screening questionnaire (Fig. ). A total of 5524 participants were eligible for inclusion and randomised and allocated to the control and experimental conditions ( n = 3695) or to the research conditions of another project ( n = 1829). (Netherlands Trial Register registration number NL8616). Overall, 547 participants were able to complete the virtual shopping (15%).
Participants who completed the shopping were on average younger (mean = 48·3, SD= 16·2 y) compared with those who dropped out of the study (mean= 57·4, SD= 15·7 y) (Supplemental Table ). Moreover, participants included in the study were more often higher educated (50%) compared to those who dropped out of the study (29%). After excluding non-representative shops ( n = 14), the final sample for analysis included 533 participants ( n = 153 for the ‘Control condition’, n = 133 for the ‘Price condition’, n = 126 for the ‘Information nudge condition’ and n 1 . Descriptive statistics show that 9·8% of the participants in the ‘Control condition’ did not purchase meat items in the virtual supermarket. In the ‘Price condition’, ‘Information nudge condition' and ‘Combination condition’, 12·0, 9·5 and 15·7% of participants did not purchase meat products, respectively.
Fig. 1 Flowchart of enrolment and allocation of the study participants. *1829 participants were randomised for the purpose of another project (Netherlands Trial Register registration number NL8616) .