Cluster-based policy design for rooftop solar deployment in residential buildings: A decision-support framework for low-carbon built environments

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a novel ML-based segmentation of prosumers in the Middle East, offering comparative insights for cross-country analysis of solar adoption drivers.

🏢実務担当者:Corporate sustainability teams in energy or construction sectors can use the segmentation approach to design targeted offers for different household profiles in residential solar markets.

🏛政策担当者:Policymakers can apply the cluster-based framework to design socially inclusive solar incentive programs that address diverse household motivations beyond cost.

The global transition toward decentralized energy systems is reshaping households into active prosumers. The motivations driving household participation in solar energy adoption extend beyond purely economic considerations and increasingly involve social, cultural, and technological factors. This study investigates the determinants of prosumerism in Iran and explores how potential prosumers can be segmented to support more effective and socially inclusive energy policies. Using survey data from 2532 households, a machine-learning approach based on hierarchical agglomerative clustering was applied to identify distinct prosumer profiles according to demographic, economic, attitudinal, social, and technological characteristics. Results show that household engagement with rooftop PV systems is shaped by a combination of cost-saving motivations, environmental concerns, social influence, technological affinity, and trust rather than financial incentives alone. The results reveal five distinct clusters of potential prosumers. Some clusters demonstrate strong pro-environmental attitudes and high willingness to adopt innovative energy technologies. Building on these insights, the study proposes a cluster-based policy framework that aligns targeted policy instruments with the behavioral characteristics of each group. The framework integrates financial incentives, non-financial support mechanisms, and market-based instruments to enhance participation across diverse household segments. This research contributes to the literature by providing one of the first large-scale empirical analyses of solar prosumer heterogeneity in the Middle East and by linking machine-learning-based segmentation with actionable policy design. The findings offer practical guidance for policymakers seeking to accelerate residential solar adoption while fostering inclusive and socially responsive energy transitions. • Machine-learning clustering of 2532 Iranian households reveals five distinct solar prosumer profiles in residential buildings. • Adoption of rooftop PV is shaped by combined financial, social, cultural, and technology-orientation factors rather than cost alone. • A cluster-specific policy instruments is developed, integrating financial, non-financial, and market-based instruments. • Results provide a decision-support framework for designing socially inclusive and adaptive low-carbon strategies in the building sector. • Findings advance understanding of socially innovative prosumerism and support equitable decarbonization of residential built environments.

• openalex https://doi.org/10.1016/j.esr.2026.102251first seen 2026-05-15 17:19:19