Artificial Intelligence and the Prospects for Net-Zero Energy and Net-Zero Carbon Buildings: A Science Mapping Analysis Using Digital Twins and Geographic Information Systems
TL;DR
A dual framework is proposed that aligns digital twins and GIS as complementary implementation platforms for connecting AI with net-zero building objectives, providing valuable insights into the intellectual structures creating intelligent, energy-efficient, and carbon-neutral built environments.
Abstract
This study explores the relationship between Artificial Intelligence (AI) and net-zero carbon buildings (NZCBs) and net-zero energy buildings (NZEBs) over the last decade. A thematic evolution has been observed in this research area, shifting from conventional optimization towards more advanced digital, intelligent, and decarbonized infrastructure. Co-occurrence, clustering, thematic evolution, network, and visualization justify this science mapping analysis at regular intervals (2015–2018, 2019–2022, and 2023–2026). Digital twins (DTs) have been identified as the dominant theme in strategic analysis, integrating Building Information Modeling (BIM), sensors, communication networks, and AI algorithms. In contrast, there has been the emergence of GIS as a complementary platform for extending AI applications beyond individual buildings to neighborhood, city, and regional scales through carbon mapping, life-cycle assessment, energy storage planning, and spatial decision-making. The analysis highlights AI as supporting technology rather than an isolated research theme, managing building information through digital twins and facilitating urban-scale decarbonization through GIS. The novelty of this study lies in proposing a dual framework that aligns digital twins and GIS as complementary implementation platforms for connecting AI with net-zero building objectives. The developed framework provides valuable insights into the intellectual structures creating intelligent, energy-efficient, and carbon-neutral built environments.
