Digital Twins in the Architectural Design Stage for Sustainable Net-Zero Buildings: A Systematic Review of Frameworks, Tools and Research Gaps

Abstract

Early architectural decisions shape building energy demand and life cycle carbon, yet digital support remains fragmented across modelling, simulation and performance workflows. This systematic review examines how digital twin (DT) frameworks, tools and workflows are applied at the architectural design stage to support net-zero building performance. It investigates whether design-stage digital twins function as decision support systems or remain BIM-plus-simulation workflows carrying a twin label. Following PRISMA 2020, Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink and Taylor & Francis Online were searched, supplemented by citation searching. Searches were completed on 28 March 2026 and restricted to English-language, peer-reviewed publications meeting eligibility criteria. Thirty-three studies formed the analysed corpus, comprising 10 Tier 1 core digital twin studies, 15 Tier 2 DT-oriented studies and 8 Tier 3 DT-enabling studies, while 19 review and contextual sources supported framing. Studies were coded by DT conceptualisation, framework type, enabling technology, life cycle stage, net-zero indicator and validation approach. Findings were synthesised descriptively and thematically, and evidence maturity was assessed using a six-domain appraisal covering reporting quality, digital twin completeness, design-stage relevance, validation quality, reproducibility and architect usability. Only 11 studies, representing 33% of the corpus, were anchored in concept or schematic design. BIM, building-performance simulation and parametric or generative modelling were dominant, while IoT and AI or machine learning supported prediction, surrogate modelling and control. Energy was addressed in 26 studies and thermal comfort in 10, whereas embodied carbon, daylight, renewable generation and indoor air quality received limited attention. Eight studies were classified in the ‘Measured/large empirical’ validation class. Methodological heterogeneity and limited empirical validation precluded meta-analysis. Design-stage digital twins remain emerging rather than mature decision support systems. The review was retrospectively registered on the Open Science Framework (DOI: 10.17605/OSF.IO/N5Z8H) and received no external funding.