9th International Conference LCA of Food San Francisco, USA 8-10 October 2014
FoodPrints of Households
Dominik Saner1,2, Claudio Beretta1, Boris Jäggi3, Ronnie Juraske1, Franziska Stoessel1 Stefanie Hellweg1,*
1
ETH Zurich, Institute of Environmental Engineering, 8093 Zurich, Switzerland
Post CH AG, 3030 Bern, Switzerland
3 ETH Zurich, Institute for Transport Planning and Systems, 8093 Zurich, Switzerland
 Corresponding author. E-mail: [email protected]
2
ABSTRACT
We used data from the Swiss household budget survey and applied multiple linear regressions based on generalized linear models to
model the at-home consumption of food products and beverages of individual households. Seven household characteristics such as size,
income, and educational level served as input variables for the regressions. The food products and beverages demands of 3,238 individual
households of a Swiss municipality were environmentally assessed with life cycle assessment. Carbon footprints per household and year
vary from 84 kg CO2-eq. to 4.97 t CO2-eq. with a mean value of 1,098 kg CO2-eq. This variability is significantly smaller than the footprint variability for the consumption areas of housing and mobility in Switzerland, where 25% of the people are responsible for 50% of
the environmental impacts. Differences between high- and low-impact households can be primarily explained by differences in meat and
dairy consumption.
Keywords: Household food impacts, Multiple Linear Regression, Generalized Linear Models
1. Introduction
Together with housing and mobility, food is one of the three environmentally most relevant areas of consumption (Tukker and Jansen, 2006). Households form the smallest organizational units in society and many decisions about food consumption are taken on this level. Understanding the drivers of household food consumption is thus important to design effective policy measures. Existing studies often work with average household
demands (Jungbluth et al., 2011) or artificial diets (Dalgaard et al., 2007). Only few studies have considered variability between different household consumption patterns (Girod and De Haan, 2009).
In this study we present a way to model the amount of food products and beverages consumption for individual households on a monthly basis. The model relies on multiple linear regression performed on data of household surveys and is able to predict demand with the help of seven explanatory household parameters.
2. Methods
We used multiple linear regressions to establish relationships between household characteristics and consumption of different food products and beverages. We distinguished between 44 different comestible good categories of unprocessed and pre-processed food products and beverages. The regression was performed with generalized linear models (GLM) using data from the Swiss household budget survey (HBS) (Swiss Federal
Statistical Office (SFSO), 2012b).
2.1. Base data
A household budget survey is an inquiry of household financial budgets and expenditures. Such surveys are
conducted in all EU states and many other countries worldwide (European Commission, 2003). HBS consist
usually of three parts: a record on household variables, a record on household member variables and a revenue
and expenditure journal. Information about revenues and expenditures has to be entered by selected households
over a certain time period, usually a month. Expenditures are categorized according to the United Nations’
“Classification of Individual Consumption according to Purpose” (COICOP) (United Nations Statistics Division
2012). The Swiss version of the HBS is conducted annually from January to December in the seven major regions of Switzerland (i.e. Lake Geneva, Espace Mittelland, North-Western Switzerland, Zurich, Eastern Switzerland, Central Switzerland, and Ticino) and inquires around 10,000 households, of which usually around 30% respond.
9th International Conference LCA of Food San Francisco, USA 8-10 October 2014
2.2. Multivariate regression analysis
Linear regressions were performed for different food product and beverages categories employing GLM and
using an iteratively weighted least square algorithm for the maximum likelihood estimation of the parameters.
GLM is a unifying approach to generalize linear regression and estimate parameters from multiple linear regressions with dependent variables that are not necessarily normally distributed (Gill, 2001). GLM uses a so-called
link function to relate linear regression of independent variables to their dependent response variable. The link
functions are chosen according to the distribution of the response variable. Link functions have been formulated
for various distributions like binomial, Poisson and normal. The inverse link functions relate the estimated parameters and the explanatory variables to the dependent variable.
Characteristics that we used as independent variables for the regressions were household size (i.e. number of
household members), number of rooms in their apartment, number of apartments in their building, age of the
oldest household member, and age of the youngest household member. These variables were all given as integer
value greater or equal to zero. Monthly household income and highest education of household members were also used. They were given as categorical values. Income is scaled from 1 to 10. Category 1 is equal to 0-1,999
Swiss Francs (CHF), 2 is equal to 2,000 to 2,999 CHF, and so forth. Category 10 is equal to 10,000 CHF or
more. Education is also divided in 10 categories ranging from no education (1) to graduate degree (10) (see Supporting Information). These seven explanatory variables were chosen because they can be easily derived from
national census data.
The dependent variables of the regressions were the amounts of purchased food products and beverages per
household during the reporting period. For the modeling of household demand we pursued a two-tier approach,
in which we multiply two multiple linear regressions. The first regression estimates the probability of a good being bought and the second regression determines the bought amount. For instance, vegetarian households are not
likely to purchase meat. Some households do not drink wine and pork meat might not be consumed due to religious reasons. Thus, the probability to buy a certain good is higher for some households than for others. We applied multiple logistic regression to dichotomous demand data (i.e. the value was 1 when the purchased amount
was greater than zero and 0 otherwise). The multiplication of the probability of a household buying a certain
good and the amount of good that will be purchased, if the household buys the good, led to the expected amount
of a certain good bought by a household.
The purchase data of one household as reported in the HBS is only collected during one month. Thus, the regressions can only predict the goods demands per households and month. Therefore, we had the choice to either
fit regression models for each month of a year or to aggregate the monthly data and fit regression models for an
average month of that year. We pursued both possibilities and used always 70% of the applicable data for fitting,
15% for validation and 15% for testing.
The regressions were fitted in a stepwise fashion, meaning that explanatory variables were iteratively eliminated from the regression, if they did not significantly improve the maximum likelihood estimation. Then, the
estimated parameters were validated with the corresponding data. Spearman  , a rank order correlation coefficient, and the root mean squared error (RMSE) between observations and modeled values were calculated.
RMSE describes the standard deviation of the residuals between observed demands yˆ and modeled demands.
Each stepwise regression was performed 30 times. After each time the regression was validated. Each time
the data for fitting and validation was randomly selected. In the end the best set of estimated parameters was selected according to the combination of the highest  and the smallest RMSE.
We performed multiple linear regressions for each possible combination of normal and Poisson distributed
response variables and monthly or aggregated data. Finally, the goodness of best set of regression parameters
was tested with the testing data. Thereby, RMSE, Spearman  and the absolute difference between total observed demand of good and total modeled demand were calculated.
2.3. Case Study
A case study was performed to illustrate the possible application of the household consumption model. The
household data for the Swiss municipality Wattwil was drawn from Swiss census data for the year 2000 (Swiss
Federal Statistical Office (SFSO), 2012a). Based on the household characteristics from the census, the demands
9th International Conference LCA of Food San Francisco, USA 8-10 October 2014
for 44 different food product and beverages categories of 3,238 households in Wattwil (8,075 inhabitants) were
quantified, using the outcome of the regression analysis. For each modeled comestible good we derived life cycle impact results for their global warming potential (IPCC, 2007) (GWP, in kg CO2-eq. per kg comestible
good). Most data was taken from ecoinvent 2.2 (ecoinvent Centre, 2012) and the study of Stoessel et al. (Stoessel
et al., 2012). This study contains life cycle inventory data from different production countries, thus, we were able
to calculate average yearly supply mixes based on foreign trade statistics from the Swiss Federal Customs Administration (Swiss Federal Customs Administration, 2012). For certain comestible goods groups (e.g. meat,
fish, and dairy products) we relied on impact results from scientific articles and reports (Alig et al., 2011; Alig et
al., 2012; Büsser and Jungbluth, 2009a; Büsser and Jungbluth, 2009b; Eymann, 2012; Humbert et al., 2009;
Jungbluth, 2006; Kendall et al., 2010; Martignoni et al., 2012; Mattila et al., 2012; Nemecek et al., 2011;
Sonesson et al., 2009; Thrane, 2006).
3. Results and discussion
Figure 1 shows the observed demands of 44 food products and beverages for 3,235 nation-wide surveyed
households in the Swiss HBS for the year 2004 (left) and the modeled demands for these households (right). The
demands per households were normalized by the number of household members to adjust for different household
sizes.
Figure 1. Observed (left) and modeled (right) demands of comestible goods for data reported in the Swiss household budget survey for the year 2004. Household demands are normalized by household size and ranked from
smallest to largest demands.
A visual comparison of the two graphs (Figure 1) as well as the comparison of the five statistical parameters
sum, mean, median, minimum and maximum value suggest that the accuracy of the modeling of demands is
high. The relative differences between the statistical parameters for the observed and the modeled values are
1.2% for the sum, 1.1% for the mean, 0% for the minimum value. Only for the median (12%) and the maximum
value (28%) the relative differences were larger. The accuracy was further investigated with a residuals plot (in
9th International Conference LCA of Food San Francisco, USA 8-10 October 2014
the upper right corner of Figure 1b). It shows that the modeled values are slightly overestimated for low observed demands and more strongly underestimated for high observed demands. However, this is not a severe inaccuracy as observed high demands might also be a result of occurring events during the reporting period of that
specific household. Events like birthday parties or anniversary celebrations might have led to extraordinary large
food purchases.
Figure 2. Global warming potential induced by in-home household consumption of 44 different food product
and beverage groups in the case study municipality. The results are normalized per household member.
As we were able to reproduce the observed comestible goods demands with our two-tiered regression approach, we applied the method to a case study region. The results for the global warming potentials from athome household consumption in Wattwil are shown in Figure 2. Greenhouse gas emissions are normalized by
the number of people living in the households to adjust for different household sizes. Households are ranked according to the aggregated impact results. Results vary from 84 kg CO2 eq. to 4.97 t CO2-eq., with a median value
of 997 kg CO2-eq. and a mean of 1,098 kg CO2-eq per year. The pie chart shows the shares of products in the total GHG emissions of the municipality. The consumption of meat & fish and dairy products are responsible for
three quarters of total life cycle GHG emissions from at-home food products and beverage consumption in
Wattwil.
9th International Conference LCA of Food San Francisco, USA 8-10 October 2014
Figure 2 shows that there is variability in the food carbon footprints. Some households consume almost no
comestible goods at home, whereas others have significantly higher consumption than the rest. However, this
variability is not so great compared to the variability of household environmental impacts in other consumption
areas (Saner et al., 1013).
4. Conclusion and outlook
The food products and beverages demands of 3,238 individual households of a Swiss municipality were environmentally assessed. We found that the carbon footprints per household member and year vary from 84 kg CO2
eq. to 5 t CO2 eq. with a mean value of 1 t CO2 eq. This variability is significantly smaller than the footprint variability for the consumption areas of housing and mobility, where 25% of the people are responsible for 50% of
the environmental impacts (Saner et al., 1013). Differences between high- and low-impact households can be
primarily explained by differences meat and dairy consumption, with vegetarian households in the lowest impact
percentile, while the consumption of beverages and plant-based food was not decisive for the overall impact.
The presented study concentrated on at-home demand and supply of food products and beverages. The main
reason was that for at-home consumption physical demand data is available from the household budget survey.
However, we saw that only around 60% of food products and beverages are consumed at home. The rest is consumed in restaurants, cantinas, and takeaways, etc. Thus, future studies in this area should also focus on the
modeling of out-of-home dining.
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