Making carbon-intensive lifestyles tangible through data-driven personas

Lifestyles of individuals in high-income countries account for a substantial share of global consumptionbased CO₂ emissions. However, most lifestyle studies provide limited guidance for identifying which individuals drive emissions and through which lifestyle domains at the subnational level. This study proposes a methodological framework to translate quantitative analysis of lifestyle-related CO₂ emissions into actionable tools for the co-creation of context-sensitive climate interventions. Using survey data from 2633 individuals across the binational Lake Geneva region, we first estimated percapita carbon emissions across three lifestyle domains (mobility, housing energy, and consumption of food and goods) and then used these results as inputs to produce data-driven personas. We found that 48% of individuals accounted for 67% of total emissions, but that these high emitters did not form a single group with uniformly high emissions across domains. Instead, their carbon intensity was concentrated in specific lifestyle domains, forming domain-specific clusters (e.g., housing-related emitters). Within these clusters, we identified substantial heterogeneity, resulting in 24 statistical profiles linking emission patterns to their underlying determinants. Finally, we translated these profiles into eight data-driven personas that represent concrete and relatable situations of carbon-intensive lifestyles. These personas were used in a participatory workshop with regional policymakers and experts to deliberate on and prioritize interventions for lifestyle change. Our findings show that carbon-intensive lifestyles differ not only in where emissions are concentrated, but also in the conditions through which they are produced, enabling to improve the targeting and framing of climate interventions.

Highlights:  Proposes a framework linking emissions data to personas for intervention design  Quantifies lifestyle-related CO₂ emissions at the subnational level  Shows CO₂ emissions are more unequally distributed than income  Identifies heterogeneous, domain-specific carbon-intensive lifestyle profiles  Translates profiles into personas for stakeholder deliberation on interventions