An integrated framework for optimizing daylighting and thermal performance of subtropical secondary school buildings via measurement, simulation and machine learning

Building with low carbon emissions (low carbon building, LCB) is crucial for the construction industry’s green transition. With digital technologies (DTs) playing an increasingly pivotal role in decarbonizing the construction sector, this study explores how DTs empower LCB to reduce global CO2 emissions. To objectively identify the core gaps of existing research, this study first conducts a preliminary scoping review of 1476 peer-reviewed articles, adopting a two-step inductive approach to reveal three dominant knowledge gaps: (1) stage-agnostic analytical bias, (2) qualitative mechanism limitation, and (3) disconnected renewable energy integration. Guided by these empirically validated gaps, a three-dimensional conceptual framework (LCB solutions-digital functions-building life-cycle stages) was developed, followed by a mixed-methods systematic review (bibliometric analysis and qualitative content coding) of 73 core peer-reviewed articles. The results indicate that: (1) Artificial Intelligence (AI) and Building Information Modeling (BIM) stand as the two dominant DTs driving LCB; (2) six core quantified empowering mechanisms are identified, with tangible benefits including a 15%-20% reduction in unnecessary electricity consumption (Internet of Things-based systems) and 18% carbon emission reduction (BIM-Life cycle assessment integration); (3) the proposed framework realizes stage-specific DT-LCB matching while integrating renewable energy synergy and carbon cost optimization. Findings provide stage-specific technical benchmarks for practitioners, targeted incentives for policymakers, and practice-driven research and development guidance for technology developers, advancing beyond fragmented DT applications to systematic, quantified, renewable energy-integrated empowerment that bridges theory–practice gaps.