Using Data Mining To Assess Environmental Impacts of Household


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Environmental Modeling

Using Data Mining to Assess Environmental Impacts of Household Consumption Behaviors Andreas Froemelt, David J. Dürrenmatt, and Stefanie Hellweg Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b01452 • Publication Date (Web): 22 Jun 2018 Downloaded from http://pubs.acs.org on June 24, 2018

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Using Data Mining to Assess Environmental

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Impacts of Household Consumption Behaviors

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Andreas Froemelt*, David J. Dürrenmatt† and Stefanie Hellweg

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Chair of Ecological Systems Design, Institute of Environmental Engineering, ETH Zurich, John-

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von-Neumann-Weg 9, 8093 Zurich, Switzerland

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KEYWORDS Household Consumption, Household Behavior, Life Cycle Assessment, Hybrid

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LCA, Data Mining, Machine Learning, Self-Organizing Map, Kohonen Map, Clustering

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ABSTRACT

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Household consumption is a main driver of economy and might be regarded as ultimately

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responsible for environmental impacts occurring over the life cycle of products and services.

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Given that purchase decisions are made on household levels and are highly behavior-driven, the

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derivation of targeted environmental measures requires an understanding of household behavior

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patterns and the resulting environmental impacts. To provide an appropriate basis in support of

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effective environmental policymaking, we propose a new approach to capture the variability of

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lifestyle-induced

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consumption patterns are derived in a two-tiered clustering that applies a Ward-clustering on top

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of a pre-conditioning self-organizing map. The environmental impacts associated with specific

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archetypical behavior are then assessed in a hybrid life cycle assessment framework. The

environmental

impacts.

Lifestyle-archetypes

representing

prevailing

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application of this approach to the Swiss Household Budget Survey reveals a global picture of

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consumption that is in line with previous studies, but also demonstrates that different archetypes

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can be found within similar socio-economic household types. The appearance of archetypes

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diverging from general macro-trends indicates that the proposed approach might be useful for an

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enhanced understanding of consumption patterns and for the future support of policymakers in

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devising effective environmental measures targeting specific consumer groups.

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1 Introduction

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Households are major drivers of the economy. Their consumption behavior triggers a

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multitude of economic activities along the supply chain of each product and service, which

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subsequently involves the use of resources and the release of emissions. Household consumption

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is estimated to be responsible for 65% of global greenhouse gas emissions and 50% to 80% of

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total land, material, and water use1. The United Nation’s Sustainable Development Goal 12

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(“Sustainable Consumption and Production”)2 demonstrates a large consensus that today’s

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consumption patterns are unsustainable and changes in consumer behavior are urgently needed3–

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11

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complex economic, social, technological and cultural systems. In addition to informing

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households about their environmental impacts, policymakers should frame an enabling

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environment for individuals to change towards more sustainable lifestyles4,6,8,12.

. However, changing household consumption behavior is challenging4,6,12, as it is embedded in

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Several studies quantified environmental impacts induced by household consumption (see

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e.g.7,8,13–15 for reviews). While many studies focus on a national average household and on

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identifying environmental priorities of different consumption areas1,3,7,13,14,16,17, several

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researchers acknowledge the importance of investigating the environmental consequences of

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different household groups (e.g.5,7,10,11,18–25). Being highly influenced by socio-cultural and

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economic factors, as well as driven by individual preferences, household behavior and

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consequential purchase decisions are diverse, and “one-size-fits-all”-recommendations are likely

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to fail5,11,26. Therefore, understanding the variability of consumption patterns and associated

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environmental impacts is required for devising targeted environmental policies.

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Studies attempting to capture this lifestyle-induced variability usually combine household

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budget surveys (also called consumer expenditure surveys) with environmentally-extended

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input-output-models (EEIOM) and then assess the environmental impacts of different socio-

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economic cohorts3,5,9–11,17,20,22,23,26, or fit regression models with socio-economic characteristics

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as explanatory variables24,27. The findings of both (sometimes combined) approaches are very

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insightful, especially if the applied regression models aim at explaining the drivers of

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environmental consequences10,16,20,22,23,25,26. However, Girod and De Haan19 found that there

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might still be significant variability of behavior within investigated household types. This raises

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the question if the use of household segments that are pre-defined solely based on socio-

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economic characteristics might prevent recognition of important behavioral patterns by assuming

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all households within a segment behaving similarly. How could the support for policymaking be

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improved, especially in view of recent calls to increase the involvement of behavioral economics

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and psychology when deriving environmental measures4,12,28? Building upon the mentioned

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lifestyle-studies, we propose a new approach: Instead of pre-defined household segments, we

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suggest deriving clusters of households which are not only based on socio-economic aspects but

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also on real observed behavior. The proposed two-stage clustering allows for studying behavior-

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associated environmental impacts in the context of total consumption and is simultaneously able

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to capture non-linear effects. This approach thus allows for investigating the nature and

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implications of different household behavior in detail. The emergence of archetypes might then

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form a new information basis to derive environmental policies tailored to actual consumption

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patterns.

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The goal of this article is twofold: First, we will demonstrate the clustering of household

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behavior by applying our approach to the Swiss Household Budget Survey (HBS). Second, this

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application will result in ready-to-use consumption archetypes with associated environmental

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modeling for Switzerland. While the transferability of the Swiss archetypes to other countries is

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unclear, the proposed methods can definitely be applied to different expenditure surveys.

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2 Methodology

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Grouping households based on their characteristics and on their consumption behavior

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represents the core of our approach to studying household environmental impacts. Utilizing

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groups of households is also important because of the so-called “infrequency of purchase

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problem”22,25 that is encountered when working with expenditure surveys in which households

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participate only during a limited time period. To obtain a representative picture of a certain

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population group’s consumption behavior, in which infrequent or season-specific purchases

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average out, several similar households need to be clustered. In previous lifestyle-studies, this is

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implicitly solved by averaging over pre-defined household segments. In our approach (Figure 1),

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we first form groups of similarly-behaving households and then in a second step derive

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archetypes representing the average behavior of these groups. Finally, the environmental impacts

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of these archetypes will be assessed by means of a hybrid life cycle assessment (LCA) that

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sources environmental background data from both EEIOM and process-based life cycle

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inventory databases. With a hybrid LCA framework, known issues with EEIOM (e.g. the

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“product quality problem”7,9,18,20,22,25) can be partly overcome by using physical functional units,

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and EEIOM can complement missing data of process-based LCA7. Furthermore, the applied

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LCA-modeling will also allow for the computation of different impact assessment methods and

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not only carbon footprints or energy requirements as done in previous studies3,5,9,10,13,16–20,22,24,29.

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Thus, the chosen LCA-approach might also help to reveal potential burden shifts induced by

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planned environmental measures. Although suggested by several authors1,3,13,15, the application

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of hybrid LCA is – to our knowledge – rare. That said, it would still be possible to directly

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couple the archetypes with EEIOM or even more sophisticated macro-economic models as used

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e.g. in21,30.

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All computation steps will be described in more detail below (see also Figure 1).

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Figure 1. Simplified flow scheme providing a synopsis of the whole modeling approach. For

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each step, sections of the main article or the Supporting Information (SI) are indicated in which

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more detailed descriptions can be found.

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2.1 Consumption Data

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The main data source of this study is the Swiss HBS31 (2009-2011) which provides detailed

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information on the characteristics and consumption behavior for 9734 households. Households

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participating in the survey report on daily expenditures, income, and quantities of bought goods

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(e.g. for food) during one month. In addition, they also report on periodic expenditures (e.g.

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newspaper subscriptions), possession of durable goods and on extraordinary purchases or

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revenues in the last few months (e.g. buying a car within the last year). Data on household

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characteristics and on household members are also collected. For each household, the final

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dataset comprises statistics on 20 different durable goods, 8 income categories (plus 4 on

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aggregated levels), 19 household variables, 6 attributes for each household member and 356

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(plus 175 on aggregated levels) consumption categories classified based on the United Nations’

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“Classification of Individual Consumption according to Purpose” (COICOP)32. For consumption

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areas, purchased amounts in liters or kilograms are available for 92 categories (plus 14 on

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aggregated levels). Further information on the categorization and a list of survey attributes is

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provided in the SI.

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2.2 Pre-Processing of Consumption Data

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The present section describes how variables needed for the computations in 2.3 (pattern recognition) and 2.4 (LCA) were created based on the original HBS-data.

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Besides converting categorical variables to numerical data by a set of binary variables, several

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count-statistics were created to better compare household member information (see SI).

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Moreover, for some categories, more detailed information than specified in the HBS-data is

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required to apply LCA (cf. section 2.4). This particularly concerns housing-related categories

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and public transport demand. With regard to the first, especially tenants typically do not have full

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information on the exact breakdown of their utility bills into refuse and sewage collection, water

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supply, electricity, and heating. To impute this missing information, a modeling approach using

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K-Nearest-Neighbor-Regression33, Random-Forest-Regression34,35 and LASSO-Regression36

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was employed (see SI). The predicted data was then converted to quantities by means of price

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data37–40. These prices were retrieved as close as possible to the specific circumstances of each

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household by taking into account household type, location, and survey year. The pre-processed

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HBS-data was then validated against national statistics40–44 (cf. SI).

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The demand for public transport is a second issue. While kilometers driven by car can be

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estimated with liters of fuel purchased, HBS-information on public transport mainly relates to

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season tickets and travel card expenditures, thereby lacking information on effectively driven

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kilometers by public transport. Therefore, this demand was estimated for each household using

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data from the Swiss Mobility Microcensus 201045, which provides detailed information on the

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mobility behavior of the Swiss population (see SI).

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2.3 Pattern Recognition and Clustering of Households

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2.3.1 Preparation for Pattern Recognition

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Due to seasonality and storage effects, the survey month might bias the results of individual

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households in the HBS31. Consequently, the determination of household clusters with similar

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characteristics and behavior requires the dataset to be pre-processed and filtered for household

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features which make similar behavior identifiable independent of the month in which the survey

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took place.

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A flow scheme in SI-section 3.1 visualizes the following preparatory step. Each HBS-attribute

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was judged separately whether it shall be included for pattern recognition or not. Note that

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exclusion only concerns the pattern recognition steps in sections 2.3.2 and 2.3.3, while the

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deduction of archetypes in 2.3.4 will resort to all available attributes. Household characteristics

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including the count-statistics of 2.2, durable goods statistics, periodic expenditures and revenues

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were all considered in the pattern recognition. In contrast, daily purchase attributes were only

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included if they are bought on a regular basis and not stored for more than a month. Otherwise, it

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was appraised if the attribute’s inclusion within an aggregated level is reasonable (e.g. if an

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aggregated attribute such as “fruits” shall rather be included than “grapes” which are rarely

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bought from January to June in Switzerland).

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In a next step, all candidate attributes were checked for seasonality. The application of

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ANOVA46 and Kruskal-Wallis-tests47 revealed if the influence of the survey month is

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statistically significant. In case of statistically significance, the respective attribute was corrected

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for seasonality (see SI-section 3.1).

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The final dataset for pattern recognition comprises 157 attributes in total, thereof 85

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consumption and income categories.

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2.3.2 Self-Organizing Map (SOM)

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A two-tiered approach was applied to find patterns in the HBS-data based on which the

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households shall be grouped. In a first stage, a self-organizing map (SOM)48,49 pre-conditioned

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and reduced the dataset, which was then clustered in a second stage. Such two-step clusterings

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demonstrated to perform well and are robust even in the case of noisy and high-dimensional

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datasets50–53.

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The SOM was proposed by Kohonen48,49 and belongs to the class of unsupervised artificial

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neural networks. It generally combines vector quantization and non-linear projection to a lower-

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dimensional space; usually to a discrete 2D-lattice of neurons. Thereby, the SOM learns the

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patterns in the dataset in an ordered fashion and is thus able to preserve the topology of the data.

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This means that neighboring neurons in the map have similar characteristics. A prototype vector

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with the same dimension as the vectors of the input dataset is associated with each neuron.

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During the training phase of the SOM, this set of prototype vectors is optimized to become a

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representative substitute for the original dataset. The resulting map is thus a reduced, but still

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representative dataset that is not only smoothed with regard to noise but also facilitates the

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recognition of patterns, be it for subsequent clustering algorithms or visually for the human eye

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(see component maps in the SI).

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For the present study, the SOM was tuned by generating several SOMs with different

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parameters (number of neurons, arrangement of neurons, initial and final neighborhood radius,

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neighborhood function, and number of epochs) based on literature recommendations48,49,54–56 and

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then by choosing the model with a topographic error close to zero and the lowest quantization

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error56. The topographic error evaluates the order of the map (e.g. if it is twisted) and represents

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the share of samples in the input dataset for which the first and second closest neurons are not

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adjacent in the map56, while the quantization error judges the representativeness and thus the

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accuracy of the map56. The final map consists of 987 neurons arranged in a 21:47-planar-lattice

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(see SI for more tuning information and a short introduction to SOMs).

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2.3.3 Clustering

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In the second stage of pattern recognition, we apply clustering algorithms on top of the SOM

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to form groups of neurons. Since each clustering technique has its strengths and weaknesses, two

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well-known approaches, which differ in their basic principles, were tested and evaluated: K-

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Means57–59 and agglomerative clustering60,61. While k, the number of clusters to be built, was the

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only tuning parameter for K-Means, agglomerative clustering was run in different combinations

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of affinity metrics (e.g. Euclidean distance, L1-norm) and linkage criteria (Ward62 and average).

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The evaluation of clusters – and thus the “best” choice of clustering techniques and associated

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parameters – is not a trivial task if the ground truth is unknown63. For the present study, we

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mainly focused on two performance methods: Silhouette coefficients (S)64 and U-Matrix. S

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relates the distances between one point in a cluster and all other points in the same cluster to the

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distances from that point to all points in the second closest cluster. The so-called U-Matrix

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(unified distance matrix) is an important visualization of a SOM and supports clustering on top

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of the map. At the map position of each neuron, an U-Matrix depicts the sum of distances in the

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high-dimensional space between the prototype vector of the respective neuron and the prototype

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vectors of all adjacent neurons52,65,66. Large U-heights indicate that a neuron’s prototype vector is

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distant from others, while small U-heights are associated with prototypes that are surrounded by

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other vectors in the data space66. The U-Matrix thus suggests visually which neurons should be

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grouped together to form clusters.

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The quest for the best clustering was subdivided into two parts (see SI for details): First, a pre-

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selection of parameters within each approach was conducted mainly based on S. Afterwards, a

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detailed comparison of the two alternatives and fixing the number of clusters for both was based

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on the U-Matrix. For this, the cluster borders were projected on the U-Matrix and the clustering

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methods were judged by their ability to redraw the visible groupings of neurons in the U-Matrix.

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Additionally, two other criteria broadened the information basis for the final decision: ANOVA-

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tests were run for each attribute to obtain an impression if reasonable results are produced.

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Second, the number of households per cluster was determined to get some indication about the

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representativeness of the clusters. Considering all these criteria, an agglomerative clustering

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technique with Ward-linkage, which produces 34 clusters, was finally selected. Furthermore, the

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applied agglomerative clustering implementation67 allows for including connectivity constraints,

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meaning that only clusters which are adjacent on the map can be merged by the algorithm. This

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ability was seen as another advantage over K-Means.

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2.3.4 Deriving Consumption Archetypes

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Considering the statistical basis of the analyses of the Federal Statistical Office31, we assumed

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a cluster to be representative for a population’s group if at least 130 HBS-households are

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member of this cluster. Since not all clusters built in 2.3.3 comply with this criterion, some post-

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processing in the sense of manually merging clusters was needed. Indeed, this merging is also

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justified by the dendrogram in the SI which shows a blur between 34 and 24 clusters, indicating

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that some clusters are close to each other. Cutting the dendrogram at different positions might be

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reasonable in the presence of sub-clusters51 (see SI for more reasons). Therefore, starting with 34

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clusters, all clusters with less than 130 households were merged with adjacent clusters if these

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merges happen in the dendrogram between 24 and 34 clusters. This resulted in 26 fair clusters

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and 2 clusters with less than 130 households. These two clusters will continue to be part of the

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subsequent analysis, but they will be marked accordingly.

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The behavior-archetypes are now constituted by the centroids of the clusters. Note that the

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computation of the clusters’ centroids follows the averaging-procedure of the Federal Statistical

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Office and includes a representativeness-weight31. Furthermore, the attributes which were

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filtered out in 2.3.1 are now re-used and also entered the vector of the archetypes.

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2.4 LCA-Modeling

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The final modeling step comprised the coupling of the archetypes’ demands with detailed life

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cycle background data in order to quantify the environmental impacts of the archetypes’

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consumption behavior. The overall functional unit for the LCA was chosen to be one year of

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household consumption. The life cycle inventory data was extracted from three well-known and

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transparent databases in the following priority order: ecoinvent v3.368, Agribalyse v1.269, and

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EXIOBASE v2.270,71.

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The functional units of ecoinvent- and Agribalyse-activities require quantities instead of

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expenditures. While section 2.2 prepared housing- and public-transport-related categories for this

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purpose, conversions based on price data (e.g.37–39,72) or further information73 was necessary for

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other consumption areas. Fortunately, for almost all food categories, quantities are directly

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available in the HBS-dataset. The process models for processed food closely followed the

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modeling of Walker et al.74, but adjusted to Swiss conditions. Generally, we always attempted to

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adapt the process models as close as possible to the domestic conditions of Switzerland. For

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instance, Swiss market activities for food were constructed based on the import statistics

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provided by Scherer and Pfister75 and car fleets and energy mixes for heating technologies were

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based on national statistics40,76.

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The creation of process models with EXIOBASE-sectors including the conversion of

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purchaser-prices (HBS) to basic-prices (EXIOBASE) generally followed the suggestions of

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Steen-Olsen et al.3

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Finally, it needs mentioning that only environmental impacts directly associated with a certain

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household’s behavior were considered. For instance, only direct spending on education or health

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care and thus retraceable to a specific household were taken into account. This leads to an

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underestimation of impacts from “health” and “education” since the Swiss education system is

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largely financed by the government, while health expenses are usually covered by insurances

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whose premiums are not necessarily indicative of the health care utilization of households and

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thus of the effectively caused impacts. Furthermore, governmental consumption was not re-

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distributed to households as done in other studies77,78. Details of the LCA-modeling are disclosed

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in the SI.

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3 Results and Discussion

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3.1 Drivers of Clustering

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The U-Matrix and the final clustering are presented in Figure 2. The two clusters not reaching

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full representativeness are named “OA” and “OB”, while all other clusters are labelled with

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random letters. The illustration of clusters on top of the SOM together with the distance

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information given in the U-Matrix also reveals relationships between clusters. For instance, “N”

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will be more similar to the adjacent “O” than to “J”.

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Figure 2. Top: U-Matrix of the SOM. Contours were inserted to improve visibility of distances.

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Note that a pixel in the map corresponds to the map position of a neuron; Bottom: Final

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clustering on top of the SOM. The clusters “OA” and “OB” are the clusters with less than 130

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households.

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Building upon the clustering selection procedure in 2.3.3, the quality of the final clustering was

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further analyzed by a visual appraisal of the 95%-confidence intervals of the clusters’ means of

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each attribute and by the ANOVA-tests (see section 2.3.3 and SI). These tests indicated that for

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all attributes there is at least one cluster significantly differing from the others (largest p-value:

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2.45E-14). ANOVA and the qualitative visual judgment support the reasonability of the final

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clustering. In addition, ANOVA provides a possibility to analyze the underlying drivers of the

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clustering process. An attribute’s test statistics (F-values) can be considered as a measure of

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“distinction power”. Among the top 20 distinctive attributes, there are, in particular, geographic

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variables and employment status, but also variables related to household size, consumption areas,

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and durable goods statistics. Attributes which are rated to be least important for forming the

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clusters are either variables which concern only few households (e.g. university fees) or

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consumption areas for which obviously less distinct patterns could be found (e.g. consumption of

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“coffee, tea and cocoa”). All results can be found in the SI.

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3.2 Individual Archetypes and their Interrelations

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A simplified description of all archetypes is available in Table 1. In this table, the clusters’

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centroids are categorized according to income and average number of persons per household.

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However, note that the clusters emerged from many different – including also non-socio-

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economic – attributes. The attributes presented in Table 1 and those used in the following

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analysis are thus just of an indicative nature meant to help better grasp the archetypes (see also

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additional details and results in the SI).

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Table 1. Simplified categorization of archetypes (clusters’ centroids) along the axes of income

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(vertical) and number of persons per household (horizontal). The numbers in parentheses show

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income in Swiss Francs per month and average numbers of persons per household respectively.

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Single-person households (1.1 - 1.3)

Small households (1.5 - 1.7)

Very high income (>19000)

Two-persons households

OB (around retirement)

High income (13000 - 19000)

Q (divorced middle-aged males)

Families (3.4 - 3.6)

Large families (>4)

S (selfemployed; some with primary school kids/ teenagers)

OA (homeowners with primary school kids/ teenagers)

L (with primary school kids/ teenagers)

C (rather young adults with babies)

J (homeowners with teenagers; "technophile")

K (SwissItalian; with primary school kids/ teenagers; homeowners)

F (SwissFrench; with teenagers; tenants)

A (homeowners with primary school kids)

P (SwissGerman; around retirement) X (SwissGerman; Zurich; around retirement, older than P)

Medium income (7800 - 9500)

O (SwissGerman; Zurich; well-mixed-notretired adults) R (Eastern Switzerland; well-mixed-notretired adults)

N (SwissGerman; Bern; well-mixed-notretired adults) Low income (< 5000)

Small families (2.4 - 3.2)

M (SwissFrench; around retirement)

Medium-high income (9500 - 13000)

Medium-low income (5000 - 7800)

D (young, unmarried; w/o children)

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G (North Western Switzerland; middle-aged adults w/o children)

Z (very young adults with babies)

U (Central Switzerland, well-mixed-notretired adults with children)

B (tenants with primary school kids/ teenagers; depend on other households; by trend: 1 adult and 2 children)

I (Swiss-French; well-mixed-notretired adults w/o children) W (SwissFrench; slightly older middleaged adults w/o children) E (Swiss-Italian; middle-aged adults w/o children)

Y (very old people)

T (Swiss-Italian; retired)

V (SwissGerman; retired)

H (old, widowed females)

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The environmental impacts of the individual archetypes are presented in Figure 3. Even

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though the applied LCA-modeling enables for the computation of all environmental indicators

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that are supported by the background life cycle inventory databases, only greenhouse gas

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emissions (GHG) according to IPCC 2013 (100a)79 and total endpoints of ReCiPe 2008 (H,A)80

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are discussed in the following (see SI for results of the Ecological Scarcity method81). Besides

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allowing for comparison to other studies, GHG have the highest priority in political discussions

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due to climate change, while the ReCiPe-endpoints shall help to calculate the overall

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environmental relevance of household consumption.

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In the scope of this article, only a few comparisons between some of the archetypes are

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presented. Thereby, selected examples in the aspects of income, household size, age structure,

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and footprint composition will be discussed. To support these comparisons, Figure 3 resumes

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information from Table 1 and shows some general characteristics of the archetypes such as

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income, number of persons and average age of adults and children.

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Figure 3. Top: Total annual impacts per archetype; colors illustrate income, while size of the

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markers represents prevalence of the archetypes (number of households per cluster). The red

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dashed line depicts the prevalence-weighted average (AVG). Indicative archetype characteristics

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are given in parentheses: P stands for average number of persons per archetype, while A provides

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the average age of adults/children; Bottom: Bar plots showing the composition of the

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environmental impacts on a per-capita basis. GHG (IPCC 2013, 100a) are shown in the left part

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of the figure and results of ReCiPe 2008 (total endpoints, H,A) are on the right-hand side. Ranks

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of total footprints are given in parentheses to facilitate the comparison of top and bottom figures.

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In the upper part of Figure 3, a correlation between household income and total impacts can be

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observed. However, a few high-income households (e.g. C (“family with babies”), D (“young,

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unmarried couples”) and Q (“divorced, middle-aged males”)) are an exception to this. In addition

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to income, household size is crucial for total emissions. This becomes apparent in the per-capita-

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bar plots in the lower part of Figure 3 when we observe how the order of archetypes changes.

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The archetypes with the largest incomes (OA (“very high-income family”), OB (“very high-

327

income retired couple”), S (“small families with self-employed persons”)) still spearhead the per-

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capita figure. But OB outstrips OA, and except for OA and S, most other family-archetypes (e.g.

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A (“family with primary school children”), B (“single-parent-families”), and Z (“young adults

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with babies”)) – including some with high-income (e.g. J (“family with teenagers”) or C) – can

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be found on the bottom of the per-capita-impact-ranking. In contrast, single-person households

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(e.g. H, N, O, and R) show low total annual impacts, while in a per-capita-perspective these

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archetypes move to the middle field. H (“old, widowed females”), together with V (“low-

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income, retired couple”) and Y (“low-income, very old couple”), also reveal a certain generation

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gap with regard to footprint composition: all three of these clusters – which are also neighbors in

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the SOM – are relatively older in age, have low communications and mobility impacts, and have

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higher housing energy and health impacts compared to other archetypes. This is for instance in

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contrast with the “young” D archetype, which exhibits high transport, but low housing, impacts.

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Interestingly, there are also archetypes with similar socio-economic conditions but different

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total emissions and footprint compositions. O and R are adjacent in the SOM and both are single-

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person households with similar age and income range. However, archetype O is related to urban

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households (Zurich), while R has its origin in the more rural Eastern Switzerland. R’s mobility

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impacts are higher on account of larger emissions from car-driven distances. This is, however,

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partly compensated by O’s large demand for air travel and taxi rides. Furthermore, O has larger

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impacts induced by eating out.

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Finally, the comparison of J and F reveals another interesting aspect. Both archetypes are

347

similar with regard to size, income and age (“families with teenagers”), per-capita footprints and

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compositions. However, for devising targeted measures to abate housing-induced emissions,

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there is one especially important difference between the two clusters: J-households are

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homeowners, whereas F are tenants.

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3.3 Archetypes in the Collective and Environmental Hotspots

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The analysis of the archetypes can help policymakers in identifying target groups. For

353

instance, OA and OB have large footprints and have money available to invest in

354

environmentally beneficial technologies to cover e.g. their transport and housing demand.

355

However, even though the signaling effect to target these groups may be large, they are not very

356

prevalent. Consequently, it is also important to look at the overall picture and investigate how the

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different archetypes contribute to cumulative impacts; which is in the case of OA and OB

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together less than 2.5%. Since HBS-data can be assumed to be representative for Switzerland, we

359

used the number of households per cluster as an indication of prevalence. This information is

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also illustrated by the size of the markers in the scatterplots of Figure 3. The upper part of

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Figure 4 depicts the prevalence-weighted contributions of both the archetypes and the

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consumption areas. The lower part of Figure 4 then relates the archetypes’ prevalence-weighted

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total impact shares to their per-capita-emissions in a simplified attempt to group the archetypes

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based on the prospects to target them. It becomes apparent that the three family-archetypes A, J

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and L, which are ranked 1st, 5th and 6th by frequency, are together responsible for about 27% of

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total impacts. While the per-capita-emissions are rather low for A and J, their high contribution

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to total impacts is due to their size (>4 persons per household on average) and their sheer

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abundance. In contrast, L is not only frequent, but also causes large per-capita and total

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footprints. These small, high-income and home-owning families with teenagers could be an

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interesting target group for reducing consumption impacts. However, this is just one example

371

since L’s contribution is about 8% of total emissions and further archetypes need to be targeted

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as well. Analyzing the main consumption areas in more detail, V (“low-income, retired couple”)

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and D (“young couple”) appear to be important. Being among the three archetypes with largest

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per-capita emissions for air travel and car trips, D has particularly large transport impacts and is

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also responsible for high emissions in the category “restaurants and hotels”. In contrast, V shows

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high health and housing impacts in addition to large per-capita food emissions. However, while

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the lower part of Figure 4 classifies D as a “promising target”, V seems to be a borderline case

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which requires more careful consideration.

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Figure 4. Top: Prevalence-weighted contributions of the archetypes and main consumption areas

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to total environmental impacts as well as prevalence-weighted contributions of archetypes and

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consumption-subcategories within the main consumption areas (ordered by magnitude of

383

contribution). Except for the Swiss total, only contributions with a cumulative effect of about

384

50% are displayed. Bottom: per-capita emissions in relation to prevalence-weighted

385

contributions to total impacts. The divisive horizontal and vertical lines correspond to the

386

medians of the x- and y-values respectively. Archetypes mentioned in the text are highlighted in

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red. [Non-dur gds.: Non-durable household goods; Pkg. holiday: Package holiday; SpS: Sporting

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services; CulS: Cultural services; PC: Computer, office appliances and other peripherals]

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The second perspective provided in Figure 4 (upper) identifies – just as many previous

390

studies1,3,7,14–16 – food, housing and transport as the most important consumption categories.

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While transport and housing each account for about 25% of total GHG emissions, transport takes

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the lead in ReCiPe-endpoints with 24%, closely followed by food and housing (each 21%).

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Thereby, transport is clearly dominated by car trips (about 90%) and housing by energy use

394

(about 85%). For food, dairy products (especially semi-hard/hard cheese) and meat (mainly beef)

395

are of similar importance; each with shares around 30% in the GHG-perspective and about 25%

396

with regard to ReCiPe-endpoints.

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The computed prevalence-weighted average for Switzerland totals at 9.0 t CO2-eq/person/year.

398

This is between the top-down study of Jungbluth et al.77 with 11.0 t CO2-eq/person/year and

399

8.6 t CO2-eq/person/year found by Girod and De Haan18 in another HBS-based study. Note that

400

both studies refer to a prior time period and that the original average of77 (12.8 t CO2-

401

eq/person/year) was adjusted to account for our study’s underestimation of health care and

402

educational services, and for not redistributing governmental consumption.

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3.4 Limitations of the Study

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The presented archetypes were formed based on behavior and characteristics of households.

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Thereby, the goal of our approach was to provide the most generalized basis for further

406

investigations. Yet, within these post-analyses – such as the application of LCA with a specific

407

environmental indicator – some clusters become seemingly very similar. Depending on the

408

policy goals, it may thus be reasonable to further group the archetypes according to their

409

environmental impacts.

410

Another limitation of this study pertains to the underreporting in the HBS. Although

411

participating households are closely supervised and receive advice from specialists82, previous

412

studies that have made use of consumer expenditure surveys revealed that underreporting is a

413

common issue3,22,29. This could also explain the slightly lower total carbon footprint assessed

414

here in comparison to the economy-wide study of Jungbluth et al.77

415

Finally, the applied hybrid LCA-modeling was done with great care and adjusted as much as

416

possible to Swiss conditions. Still, LCA-modeling always requires assumptions, average mixes

417

and simplifications that might affect the final results. Hereby, the uncertainties induced by the

418

conversion of expenditures to functional units via price data, the uncertainties arising from

419

converting HBS-purchaser-prices to EXIOBASE-basic-prices3,9,10,29, and the limited number of

420

biosphere flows in EXIOBASE need special mentioning. The latter is discussed in the SI and

421

may lead to a slight under-accounting of ReCiPe-endpoints. Follow-up research aims at

422

developing a framework to capture uncertainties involved in the archetype-approach.

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4 Outlook

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The proposed archetype-approach is meant to deliver important insights into households’

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consumption behavior for policymakers to derive and prioritize targeted measures. In this

426

context, our approach can also be used for identifying drivers of environmental impacts as done

427

in previous studies10,16,20,22,25,26. To demonstrate this application, a univariate correlation analysis

428

between environmental impacts and main household characteristics is presented in the SI. The

429

computation of such correlations, as well as studying the drivers for clustering and the

430

prevalence-weighted analyses, help to capture the big picture and support thus the identification

431

of general tendencies, hotspots and potential target groups of households. However, since the

432

overall trends do not necessarily apply to particular archetypes, the deduction of targeted actions

433

requires an in-depth understanding of the target groups and should thus be done on the basis of

434

individual archetypes. For this, the archetype-approach offers a predestined basis by allowing for

435

backtracking environmental impacts to the living conditions of real households and observable

436

consumption behavior.

437

While this study aimed to provide the basis for identifying strategies, the specific analyses

438

need to be done by local environmental policymakers according to their political agendas.

439

Thereby, they could follow the comprehensive framework proposed by Schanes et al.4 and also

440

consider suggestions from related research in behavioral economics and psychology28. The input

441

from these disciplines, which aim to understand motivational factors and cognitive biases, could

442

provide support to go even beyond “conventional” measures, such as taxes, regulations or

443

subsidies, and allow for profiling households. Based on this, personalized messages and

444

measures could be formulated which directly address different consumer groups in order to

445

effectively raise awareness and to encourage them to change towards more sustainable

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consumption patterns. But before investigating how people could be motivated to lower their

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environmental impacts, it is of high importance to know which archetypes prevail in the local

448

policymakers’ sphere of influence. The prevalence-weighted analyses illustrates a first attempt in

449

this direction at a national level, but the archetypes can further be used as a basis for a

450

regionalized model. In follow-up research, the archetypes will be assigned to real households

451

based on the national census within a probabilistic-classification approach. In addition, this use

452

of archetypes will simultaneously be combined with other nationwide bottom-up models such as

453

the building energy model of Buffat et al.83 and agent-based mobility models as used by Saner et

454

al.84. The final model will estimate a realistic environmental profile for each real household in

455

Switzerland and thus provide information for analyses on any desired regional scale.

456

ASSOCIATED CONTENT

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The following files are available free of charge: PDF and XLS file with detailed methods and

458

results.

459

AUTHOR INFORMATION

460

Corresponding Author

461

* Andreas Froemelt

462

ETH Zurich

463

Institute of Environmental Engineering

464

Ecological Systems Design

465

HPZ E32.2

466

John-von-Neumann-Weg 9

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CH-8093 Zurich

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[email protected]

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Phone: +41 44 633 68 40

470

Present Addresses

471

† Process Technology, BU Environmental Technology, Rittmeyer Ltd., Inwilerriedstrasse 57,

472

6340 Baar, Switzerland

473

Author Contributions

474

The manuscript was written through contributions of all authors. All authors have given approval

475

to the final version of the manuscript.

476

Funding Sources

477

SCCER Mobility, funded by Innosuisse

478

Notes

479

The authors declare no competing financial interest.

480

ACKNOWLEDGMENT

481

We would like to thank Christie Walker, Stephan Pfister and Adrian Haas for their important

482

inputs and Jonas Müller and Annina Brupbacher for their assistance. Our thanks also go to the

483

Federal Statistical Office, Kjartan Steen-Olsen and Richard Wood for their support. This

484

research project is part of the Swiss Competence Center for Energy Research SCCER Mobility

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of the Swiss Innovation Agency Innosuisse.

486

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