Beyond one-size-fits-all

Abstract

The need to decarbonize the built environment has increased policy and research attention on large-scale energy-efficient renovations (EER) in the residential sector, particularly social housing. While technical pathways for such renovations are increasingly well-defined, successful implementation depends on an often underestimated factor: the human dimension. In practice, success depends not only on technical solutions but also on tenant consent and participation. In the Netherlands, social housing associations (HAs) are required to obtain consent from at least 70% of affected tenants before proceeding with impactful EER projects. Yet, current practice often relies on uniform consent and communication strategies, implicitly treating tenants as homogeneous despite wide variation in behavioural determinants, perceptions, and personal characteristics. Drawing on behavioural insights and a tailored survey among Dutch social housing tenants, this study develops a data-driven persona framework using Latent Class Analysis (LCA). The analysis identifies five benefit-based classes and seven barrier-based classes that capture systematic heterogeneity in comfort expectations, trust, perceptions of financial risk, transaction costs, and decision preferences. These probabilistic classes are translated into actionable personas that reflect tenants' differential receptiveness to information and engagement modes, thereby enabling practitioners and policymakers to better anticipate where non-agreement risks may emerge and to design targeted intervention strategies. The studies' main contribution lies in operationalizing behavioural heterogeneity as an implementation tool for consent-based governance. It provides a scalable method for designing differentiated, targeted intervention strategies, anticipating consent dynamics, and supporting more just and effective implementation of EER projects in social housing.