Using Data Mining To Assess Environmental Impacts of Household Consumption Behaviors

Household consumption is a main driver
of economy and might be
regarded as ultimately responsible for environmental impacts occurring
over the life cycle of products and services. Given that purchase
decisions are made on household levels and are highly behavior-driven,
the derivation of targeted environmental measures requires an understanding
of household behavior patterns and the resulting environmental impacts.
To provide an appropriate basis in support of effective environmental
policymaking, we propose a new approach to capture the variability
of lifestyle-induced environmental impacts. Lifestyle-archetypes representing
prevailing consumption patterns are derived in a two-tiered clustering
that applies a Ward-clustering on top of a preconditioning self-organizing
map. The environmental impacts associated with specific archetypical
behavior are then assessed in a hybrid life cycle assessment framework.
The application of this approach to the Swiss Household Budget Survey
reveals a global picture of consumption that is in line with previous
studies, but also demonstrates that different archetypes can be found
within similar socio-economic household types. The appearance of archetypes
diverging from general macro-trends indicates that the proposed approach
might be useful for an enhanced understanding of consumption patterns
and for the future support of policymakers in devising effective environmental
measures targeting specific consumer groups.