The need to inform consumers about the health impact of their food choices is ever more pressing in a world where obesity is a growing problem. Concerns over food safety, its origins and its environmental impacts are also growing, as frequently reported in the popular press in many parts of the world. Nutritional and health information on food labels is quite well developed, but the complex nature of the information presented may hinder widespread use of the existing labels. In comparison, there has been little widespread success of carbon labels on food, and their usefulness in reducing carbon emissions from consumption is uncertain. In an attempt to address the need for clearer information on health and environmental impacts of food purchases, we present a novel dual-purpose food labelling system which provides information on both health and environmental impacts of food items. This paper presents results from a pilot study introducing a novel approach to food labelling: a simplified, combined carbon and health label to inform consumers simultaneously about the environmental and health impacts of their choices. Environmental impacts of various food categories were calculated on the basis of their relative energy use along the supply chain by using a newly designed Food Energy Index. Health impacts were based on the NuVal system developed in the USA in 2010. As part of the Norfolk Island Carbon and Health Evaluation study (NICHE), labels were designed, tested and displayed on 25 food categories for a 3-month period in the main supermarket on Norfolk Island (Australia). The in-store labelling trial was followed by a consumer survey on their attitudes to the labels. The results from this pilot trial indicate that consumers were supportive of food labelling including both environmental and health impacts, but the information provided in the dual labels was not sufficient to induce changes in consumption between food categories. We conclude that simple label design is clearly essential, and our findings warrant further investigation, including a broader study using a larger population and a wider range of food categories.
Since the 1990s, consumers have had access to food labels displaying nutritional information aimed at informing them and helping them make healthier food choices [
Carbon labelling of food is a much more recent concept with limited implementation to date. In anticipation of government regulations, some retail and food manufacturing companies have instigated carbon labelling schemes. Early schemes such as the Carbon Reduction Label launched in 2007 by the UK Department for Food and Rural Affairs (DEFRA) and the Carbon Trust have paved the way for the UK Publicly Available Standards (PAS) 2050 published in 2008, while the ISO 14067 Carbon Footprint of Products Certification has been available since 2013. These approaches use either a traffic-light system or a foot logo, combined with the quantity of carbon dioxide equivalent (CO2eq) emitted per serve or per pack of a food item. Although these efforts represent progress in the food labelling arena, there remains a range of difficulties both with consumer response and computational methodologies for carbon footprints.
There has been limited research on consumer responses to carbon labels in the shopping environment. Much of the information available is related to consumer attitudes, either derived from focus group consultations [
Only a limited number of studies have measured actual consumer behavior using actual carbon labels on food products [
Understanding the differences between consumer attitudes and actual consumption behavior is a challenge. Several studies have tried to evaluate why claims about environmental attitudes are not reflected in behavior [
In an attempt to address both the obesity epidemic and increasing carbon emissions in Australia, the federal government funded an investigation into the use of Personal Carbon Allowances (PCAs)1. As a contribution to this broader study, a combined label displaying both health and carbon information has been tested in a pilot trial, with a view to evaluating its potential to influence consumer food purchases. Building on findings from previous research, the present study introduces three novel approaches to food labeling. Firstly, the use of a dual label aims to offer consumers a rapid assessment of both health and environmental impacts on the same label. To our knowledge, such a combined approach to food labelling has not been reported in the literature to date. Secondly, the rapid assessment is realized through the synthesis of complex information into a single score for each feature, one for health and one for carbon impacts. Finally, previous studies labelled products from different brands within the same food categories [
This project was part of the Norfolk Island Carbon and Health Evaluation study (NICHE) that investigated the use of a Personal Carbon Trading (PCT) system in reducing carbon emissions and improving health outcomes. Norfolk Island (NI), a territory of Australia, is a very small island only 7 km × 5 km in size, located in the Coral Sea, 1500 km off the east coast of Australia and 1200 km off the North Coast of New Zealand. Approximately 35% of its population of some 1800 are descendants of The Bounty mutineers, with the remainder being made up of fairly equal numbers of Australian and New Zealand expatriate residents, although the island also hosts about 20,000 tourists annually. This geography means that almost all resources have to be imported. The local population is almost self-sufficient in fruit and vegetable production, with some small amounts of vegetables, meat, cheese and fish also produced for commercial purposes. To investigate shopping habits in this location, the one major supermarket provided the study location, this being the place where almost all the local population, and tourists, shopped for food. The island’s strong food culture, its high level of awareness of sustainability issues (due to their isolation and natural limitations in waste disposal and water and energy supply) and the ease of access to the whole population for information dissemination made it suitable for a food labelling trial.
Focus groups conducted on Norfolk Island leading up to the trial helped to select the food categories to be included in the trial. Food categories were chosen on the basis of frequency of purchases and the availability of a “better” (i.e. nutritional and environmental) alternative amongst those available at Foodland. Better health value foods were defined as having lower energy density, and lower fat, sugar and salt content. Better environmental value foods were defined as requiring a lesser degree of non-renewable energy in production, manufacturing and transport.
A total of 25 food categories covering 290 individual food items were included in the trial. Fresh fruit and vegetables were not included in the trial due to the special circumstances of food supply on Norfolk described above (2.1 Study population). Residents grow their own produce and as result, very little fresh fruit and vegetables are available in the supermarket. Two of the chosen categories, local bacon and yogurt had only one product in each category, and so were not retained for statistical tests. As part of a larger project, one major aim of this study was to inform consumers about the carbon and health impacts of general food groups rather than finer details within food groups. This meant that the food categories included were broad (e.g. chocolate) and did not specify brands, flavors or packaging sizes (e.g. dark, milk, containing nuts, family-size, etc.). While this particular characteristic of the study allowed for labels to be displayed on the shelf, it also meant that consumers were asked to consider much more drastic changes to their food choices (substituting chocolate for dried fruit, chips for nuts, ice cream for yoghurt, etc.). Although this was a clear disadvantage in the methodological design, the retailer involved in the study wanted to avoid the need for having individual labelling of a large number of products. Being the only main retailer on the island meant that there was no alternative to this strategy.
Initial focus groups on Norfolk Island revealed an interest in a combined label that could provide simple and easy to understand health and carbon values. Coupled with information from the literature, it was decided to design a label with a numerical score and a graphic using a traffic-light color system. For the numerical score, a scale was used between 0 (low health, high carbon) and 100 (high health, low carbon) and for the graphical element, a stick figure was used for health, while a footprint represented carbon.
For the purpose of this labelling trial, health scores were represented by the NuVal index [
Calculating carbon emission intensity for large number of specific food products is a difficult task. The process is data intensive and time consuming, as well as hindered by the absence of detailed data on carbon content at the product level, and by the increasing complexity of food supply chains. Since individual products were not the focus of this study, estimating carbon emission scores for individual products was not attempted. Instead, scores for broad food categories were calculated based on their energy intensity. Energy intensity was calculated using a Food Energy Index (FEI) developed specifically for use in this study.
Most studies which try to assess the carbon content of products use life cycle assessment [
In the development of the FEI, the relative energy impact was first calculated for five aspects of the food supply chain: agricultural production, manufacture, packaging, transport and trade. Each of these components was calculated relative to total energy use along the food chain. Weights for these five components were developed using the results of a detailed nation-wide study that estimated the change in energy use of 19 food categories in the USA between 1997 and 2002 [
With the energy impacts of these food chain components identified, along with the relative importance of each (their weights), the second step in developing the FEI involved the definition of specific sub-components. For each of these, more detailed embodied energy information was found from the literature, in the form of energy use per kg of product, where possible from a single study, thus maintaining methodological consistency across products. The energy intensities within each main component were then normalized, producing sub-component weights, using Equation (1).
Component | Weight | Sub-component | Weight | Data source |
---|---|---|---|---|
Agricultural production | 0.27 | Horticulture | 0.035 | [ |
Grains | 0.078 | |||
Poultry | 0.451 | |||
Pork | 0.647 | |||
Red meat and dairy | 0.974 | |||
Manufacture | 0.30 | Minimal | 0.270 | [ |
Moderate | 0.405 | |||
Extensive | 0.973 | |||
Packaging | 0.07 | Paper | 0.188 | [ |
Plastic | 0.743 | |||
Glass | 0.893 | |||
Multiple layers | 0.990 | |||
Transport | 0.07 | Local | 0.001 | [ |
New Zealand, sea | 0.067 | |||
Australia, sea | 0.095 | |||
Australia, air | 0.970 | |||
Trade | 0.29 | 1 node in supply chain | 0.143 | |
2 nodes in supply chain | 0.286 | |||
3 nodes in supply chain | 0.429 | |||
4 nodes in supply chain | 0.571 | |||
5 nodes in supply chain | 0.714 | |||
6 nodes in supply chain | 0.857 | |||
7 nodes in supply chain | 1.000 |
In this way, the “agricultural production” component had five sub-components relating to the nature of the main food ingredient (shown in
For the packaging component, we used published data for energy used in paper, plastic and glass packaging manufacturing [
To further facilitate consumer understanding, the FEI was converted to a similar scoring system as the NuVal index, such that low scores represented a poor outcome, while higher scores represented better outcomes. Resulting health and carbon scores for each food category are presented in
As well as individual labels, paired labels were also displayed inviting consumers to carefully consider their choices by suggesting a “healthier” or “greener” option. Of the suggested alternatives, seven offered improved health outcomes, one offered improved environmental outcomes and four improved both prospects. Labels were displayed on supermarket shelves where the target products were located, as shown in
To raise awareness about the trial and help consumers better understand the links between their food choices, their health and the environment, a comprehensive information campaign was conducted prior to, and during, the study. This included posters at the supermarket entrances, leaflets distributed to shoppers (
Food category | Number of food items in category | Carbon score (FEI) | Health score (NuVal) | |
---|---|---|---|---|
1 | Bottled water | 6 | 69 | 100 |
2 | Soft drinks, local | 4 | 55 | 1 |
3 | Soft drinks, imported | 42 | 51 | 1 |
4 | Fruit juice | 28 | 69 | 21 |
5 | Chocolate | 25 | 36 | 15 |
6 | Fruit | 0 | 95 | 85 |
7 | Dried fruit | 19 | 68 | 85 |
8 | Muesli bars | 8 | 40 | 45 |
9 | Unhealthy breakfast cereals | 32 | 26 | 6 |
10 | Porridge, muesli, weetbix | 5 | 62 | 90 |
11 | Chips | 17 | 69 | 3 |
12 | Rice crackers, sesame bars | 9 | 40 | 69 |
13 | Nuts and seeds | 24 | 64 | 95 |
14 | Milk, full cream | 4 | 47 | 84 |
15 | Milk, low fat | 2 | 47 | 100 |
16 | Cheese | 12 | 43 | 25 |
17 | Cottage cheese, feta | 3 | 43 | 35 |
18 | Prepared chicken meal | 6 | 30 | 28 |
19 | Whole/pieces of chicken | 7 | 53 | 36 |
20 | Bacon, local | 1 | 69 | 70 |
21 | Bacon, imported | 4 | 33 | 65 |
22 | Ice cream | 12 | 26 | 8 |
23 | Yoghurt | 1 | 4 | 35 |
24 | Butter | 6 | 47 | 2 |
25 | Margarine | 4 | 38 | 4 |
Total | 281 |
vouchers were offered as lottery prizes drawn from the pool of survey respondents. The lottery was conducted during a weekly morning radio show on Norfolk Island radio, on six occasions over the duration of the trial.
To further examine the acceptability of the combined health-carbon labels, a short consumer exit survey was carried out during the trial period. The survey was designed to evaluate shoppers’ attitudes to the labels being tested. The survey also aimed to test potential associations between local food and environmental sustainability. Due to the geographic isolation of Norfolk Island, “local” food items can be perceived as much fresher than imported foods sourced from far away. The survey assessed whether the positive associations with freshness and health also extended to environmental benefits.
Following pilot testing, a final survey was developed containing 19 Likert scale questions asking respondents to rate statements ranging from 1 = “strongly agree” to 7 = “strongly disagree”. Eight questions examined consumer’s attitudes to food labelling and the specific elements of the labels; eleven questions explored perceptions of sustainability in local food; and two questions required demographic information. The survey was distributed by mail to all 805 households on the island (of which 655 were occupied at the time), together with a copy of the information pamphlet.
Sales data was provided by the retailer for a six month period for a total of 281 products. This period covered the three-month trial period and the preceding three months. It was not possible to get weekly sales data, and information on prices and their variability during the trial was not made available.
Pre and post-trial sales data were compared using tests of significance. The population on the island increased by 19% between the first and last week of the trial due to seasonal visitor numbers. Visitors represented 23% of the total population when measured on the basis of tourist nights over the whole duration of the trial. According to local business operators, it is likely that this increase in visitor numbers was responsible for the 11.6% increase in supermarket sales over the same period. To better represent the actual change in sales (S) between the two periods irrespective of the population change, the relative change in sales (y) was calculated using Equations (2) and (3). Thus y = 1 will indicate no change in sales, y < 1 a drop in sales and y > 1 an increase in sales relative to the previous period.
where S = actual change in sales
x1 = sales before trial
x2 = sales during the trial
The null hypothesis posited that the test labels would have no effects on sales, as measured during the two time periods. Multiple regression analyses were used to test this; a first model used the NuVal and FEI scores as independent variables and relative change in sales as the dependent variable; a second model tested whether label color was more important than the numerical score in purchasing decisions. Label color was allocated dummy values (0 = yellow, 1 = red, 2 = green) and treated as a categorical variable. T-tests were also performed to compare changes in sales between paired food categories suggested as better alternatives. Sales analyses were carried out using PASW® Statistics 17.0.
Responses to the consumer survey were analyzed using PRIMER-E [
Sales data provided by the supermarket were a cumulative record of the number of items sold in the labelled categories for the two time periods. Results of regression analyses using relative change in sales for items with health and carbon scores are shown in
A description of change in sales according to label color is presented in
Comparing sales between specific food products allowed a more detailed look at purchasing behavior. Results of t-tests performed to compare means between a labelled product and its suggested “better” alternative are presented in
Variables | Coefficients | SE | p values | Adjusted r2 |
---|---|---|---|---|
Prior sales | 1.005 | 0.087 | 0.000 | 0.013 |
Health | 0.002 | 0.001 | 0.059 | |
Carbon | -0.002 | 0.002 | 0.342 |
Health variables | Coefficients | SE | p values | Adjusted r2 |
---|---|---|---|---|
Intercept | 0.967 | 0.092 | 0.000 | 0.007 |
Dummy 1 (Red) | −0.021 | 0.098 | 0.832 | |
Dummy 2 (Green) | 0.064 | 0.106 | 0.550 | |
Carbon variables | Coefficients | SE | p values | Adjusted r2 |
Intercept | 0.903 | 0.040 | 0.000 | 0.017 |
Dummy 1 (Red) | 0.131 | 0.072 | 0.068 | |
Dummy 2 (Green) | 0.109 | 0.059 | 0.064 |
Product pair | n food items in category | Mean change in sales | SD | t | df | p |
---|---|---|---|---|---|---|
Juice | 28 | 0.957 | 0.402 | |||
Bottled water | 6 | 1.080 | 0.510 | |||
Total | 34 | 0.979 | 0.417 | 0.651 | 32 | 0.260 |
Chips | 17 | 1.122 | 0.578 | |||
Nuts | 24 | 0.936 | 0.380 | |||
Total | 41 | 1.013 | 0.474 | 1.160 | 26 | 0.128 |
Chips | 17 | 1.122 | 0.578 | |||
Rice crackers | 9 | 0.923 | 0.330 | |||
Total | 26 | 1.053 | 0.508 | 0.949 | 24 | 0.176 |
Regular milk | 4 | 1.131 | 0.229 | |||
Lite milk | 2 | 1.232 | 0.540 | |||
Total | 6 | 1.165 | 0.304 | −0.349 | 4 | 0.372 |
Butter | 6 | 0.824 | 0.232 | |||
Margarine | 4 | 0.935 | 0.161 | |||
Total | 10 | 0.868 | 0.204 | −0.897 | 8 | 0.198 |
Block cheeses | 12 | 0.892 | 0.392 | |||
Cottage cheese | 3 | 1.444 | 0.490 | |||
Total | 15 | 1.002 | 0.456 | −2.094 | 13 | 0.028 |
Imported soft drinks | 42 | 0.778 | 0.436 | |||
Local soft drinks | 4 | 0.632 | 0.579 | |||
Total | 46 | 0.765 | 0.444 | 0.625 | 44 | 0.267 |
Chocolate | 24 | 1.023 | 0.503 | |||
Dried fruit | 19 | 1.200 | 0.615 | |||
Total | 43 | 1.101 | 0.555 | −1.038 | 41 | 0.153 |
Unhealthy breakfast cereals | 32 | 1.051 | 0.372 | |||
Healthy breakfast cereals | 5 | 0.849 | 0.342 | |||
Total | 37 | 1.024 | 0.370 | 1.139 | 35 | 0.131 |
Prepared chicken meals | 6 | 1.229 | 0.507 | |||
Whole chicken | 7 | 0.773 | 0.291 | |||
Total | 13 | 0.983 | 0.453 | 2.029 | 11 | 0.034 |
categories with suggested alternatives, 2 incurred changes in sales that were statistically significant (p < 0.05). Prepared chicken meals had lower health and carbon scores than whole chickens, yet their sales increased by 23% while whole chicken sales decreased by 23% (t(2.029), p = 0.034). Block cheeses had a lower health score but the same carbon score as cottage cheese and their sales decreased by 11% while sales of cottage cheese increased by 44% (t(−2.094), p = 0.028).
This preliminary investigation generated 66 responses from Norfolk Island households representing 10% of households occupied during the survey period. Male respondents made up 17% of all responses, females 83%. The mean respondent age was 51 - 60 years, which is above the population median age of 46. All “agree” and “disagree” responses were grouped and their distribution is presented in
Proportion of responses | ||||
---|---|---|---|---|
Survey question | Agree | Neither | Disagree | |
About the labels | ||||
1 | The NICHE labels were clear and easy to understand. | 0.86 | 0.09 | 0.05 |
2 | The NICHE labels have helped me to know more about the carbon impact of my food purchases. | 0.86 | 0.09 | 0.05 |
3 | Information about the energy/carbon content of food items may influence my purchasing decisions. | 0.86 | 0.08 | 0.06 |
4 | The color scheme in the NICHE labels made them easy to understand. | 0.91 | 0.06 | 0.03 |
5 | The meaning of the numerical scales on the NICHE labels was easy to understand. | 0.83 | 0.12 | 0.05 |
6 | The combination of both health and energy/carbon information on the NICHE label would improve existing food labelling. | 0.89 | 0.08 | 0.03 |
7 | Energy content of a product is more meaningful to me than a carbon emissions measure. | 0.59 | 0.30 | 0.11 |
8 | Food labels must be clear and simple to be useful. | 1.00 | 0.00 | 0.00 |
About local food | ||||
9 | Buying local food is better for the environment. | 0.98 | 0.02 | 0.00 |
10 | Knowing where food comes from matters to me. | 0.95 | 0.05 | 0.00 |
11 | Locally produced food will have a lower energy/carbon footprint. | 0.94 | 0.05 | 0.02 |
12 | Food safety is a good reason for buying local products. | 0.88 | 0.12 | 0.00 |
13 | Freshness is a good reason for buying local products. | 0.97 | 0.02 | 0.02 |
14 | Nutrition/health is a good reason for buying local products. | 0.98 | 0.00 | 0.02 |
15 | Lower prices is a good reason for buying local products. | 0.75 | 0.15 | 0.09 |
16 | Supporting local producers is a good reason for buying local products. | 0.98 | 0.00 | 0.02 |
17 | Supporting the environment is a good reason for buying local products. | 0.97 | 0.02 | 0.02 |
18 | Cost is a barrier to purchasing more local products. | 0.55 | 0.17 | 0.28 |
19 | Restricted availability of fresh produce seriously limits my food choices. | 0.68 | 0.11 | 0.21 |
Gender had no effect on respondents’ attitudes to the labels or to local food (p < 0.05). Age had no effect on attitudes to the labels (p = 0.05) but there was some difference in attitudes to local food between age groups (R = 0.137, p = 4.8%). Respondents aged 30 to 49 years differed from those aged 50 to >70 years. Attitudes to the NICHE labels had a strong positive correlation to attitudes to local food (p = 0.01). Respondents with positive attitudes to the labels also valued local food and perceived it as having health, environmental and economic benefits.
The current study assessed the acceptability of simplified, combined food labelling, incorporating both health and carbon emissions impacts through an analysis of food sales and a consumer survey on Norfolk Island. Food sales data were compared over two time periods, firstly with no food labels and subsequently where labels were provided for a selection of 25 food categories.
The Food Energy Index (FEI) was developed specifically for this project. The methodology was designed as a cost effective alternative to the more detailed Life Cycle Assessment (LCA) approach used in other studies [
There were a number of positive outcomes from the labelling trial, including a trend away from red health labels towards green labels, although this trend was not statistically significant. It is possible that a longer trial period and greater sales volumes would have revealed a stronger trend, although much of the environmental psychology literature on this subject suggests that consumers are internally inconsistent. The literature indicates that nutritional labels are effectively used by health-conscious consumers, perhaps because improved health choices provide direct personal benefit [
Carbon labelling of food is more novel than nutrition labelling, and there are too few studies to date to fully comprehend the value of carbon labels. There has also been little effort to build awareness of the carbon impact of food choices. Given that the benefits of low carbon food options are perhaps less tangible than the benefits of healthier food options, there is a real need to promote a better understanding of this issue. At least one study showed that climate friendly meals did not affect consumer satisfaction when eating out, and providing the climate impact information resulted in increased purchases of low impact meals [
The selection of food categories to include in any labelling trial is important, and the availability of a greater number of food labels improves shopper usability. In this trial, the choice of food categories was constrained by the quasi-absence of fresh fruit and vegetables in the supermarket, due to the special conditions of food provision associated with the island’s isolation (described in 2.1 Study Population). This was a serious limitation to this pilot study, as it resulted in few products representing both good health and low carbon impacts, possibly limiting consumer choices. The availability of a wider range of labelled products must be incorporated in the next phase of this work.
Some difficulties were encountered in implementing the labelling trial. Concerned about customer perceptions, the supermarket management was reluctant to display the labels directly on the shelves alongside price tags. The labels could only be positioned on top of the shelves, above the relevant products and in some cases, well above eye level. This limited the visibility of the labels to customers. In addition, the arrangement of food products along supermarket aisles was not conducive to easy comparisons between products, and it was not possible to influence this. For example, alternative products suggested as better options on the paired labels were often located in a different aisle, making direct and speedy comparisons by consumers difficult.
While it is argued that a larger range of labelled foods would encourage more effective product substitutions [
The demography of shoppers during the trial period may be another factor contributing to the weak evidence from the sales results. The high proportion of visitors (23%) on the island during the study will have influenced the purchasing record, since tourists will have different spending patterns from normal consumers living at home. In addition, none of the tourists purchasing in the supermarket had been exposed to the information campaign, or even knew about the study. The survey responses showed an over-representation of women, which suggests that (as elsewhere) women tend to be the main shopper in Norfolk Island households. In spite of this difference in numbers however, there was no significant difference in survey responses between genders.
The majority of respondents (86%) claimed that food labels were useful in informing their food choices, and indicated such labelling systems may influence their purchasing decision. According to respondents, the specific labels used in this study were also found to be an improvement on existing nutritional labelling systems, indicating current labelling may be too detailed and complicated. Consumers seemed satisfied by the label design, finding the color scheme helpful and the single, numerical score for each item meaningful.
Survey responses showed evidence of strong support for the environment and local food supply chains. Responses indicated that local food was perceived by consumers as having clear environmental benefits. Local food was thought to be better for the environment by having a lower carbon footprint and less embodied energy, and buying local food was contributing to better environmental outcomes. This is in contradiction with much of the literature about local food, where evidence of environmental benefits from shorter transport distances is not so clear [
A number of previous studies suggest that consumers appear to place value in carbon labelling [
Several previous studies point to poor carbon literacy in shoppers and difficulty in interpreting nutritional information [
This pilot study evaluated the use of a simplified, combined food labelling system that included information on both health and carbon emission impacts of food choices. The study involved an in-store labelling trial and analysis of food sales followed by a consumer survey on Norfolk Island, Australia. The study confirmed consumer interest in simplified health labelling schemes and in the novel approach of combining carbon- health information, even though sales data did not show significant changes in purchasing behavior. The survey also indicated that consumers valued the specific labels used in this study, suggesting that this novel approach to food labelling warranted further investigation.
Due to the limited scope of the study, however, it is difficult to conclude that the combination of health and carbon labelling is effective. Further work should consider a broader population and ensure that labels are better displayed, either on the products themselves, or by placing the labels at eye level, in closer proximity to the actual products. It would be worthwhile to include a greater number of matched substitute products that may generate a greater behavioral response than the products included here. The inclusion of more food options with high health benefit and low carbon impacts such as fresh fruit and vegetables would be especially desirable. Similarly, more detailed sales data would improve analytical capacity in a more comprehensive study.
This project was carried out under the auspices of the NICHE project, with funding from the Australian Research Council, contract number LP110100452, and Southern Cross University. Much assistance was provided for this work not only by the retailer Foodland and food producers in Norfolk Island, but also by the residents and tourists who participated in this study.
Pelletier, M.-C., Sullivan, C.A., Wilson, P.J., Webb, G. and Egger, G. (2016) Informing Food Consumption Choices: Innovations in Measuring and Labelling. Food and Nutrition Sciences, 7, 1149-1170. http://dx.doi.org/10.4236/fns.2016.712108