This study calculates the carbon footprint of chemical coagulants and operational energy for residential and industrial (whey digestion) wastewater treatment using ReCiPe 2016 methodology within a clearly defined system boundary from cradle to gate. Data from water treatment facilities have been analyzed to quantify environmental impacts and identify sensitive design parameters. The estimated emission of treating 1 m³ of wastewater from whey digestion (7.1195 kg CO₂ eq) is over 50 times higher than that of a residential one (0.1349 kg CO₂ eq). Life cycle impact assessment (LCIA) reveals that iron (III) chloride (40% in H₂O) and operational electricity consumption have higher impact categories compared to other design components. The uncertainty analysis indicates that electricity consumption (r = 0.4) is the dominant contributor to emissions, with a mean value of 4.22 kg CO₂-eq per m³ of wastewater treated. In contrast, iron (III) chloride emerges as the most sensitive parameter (r = 0.88) with small variations in dosing causing a disproportionately large impact on overall emissions. Therefore, the optimized use of an iron-based coagulant, the adoption of membrane electrolysis, and the integration of renewable electricity into the process supply chains have been identified as effective strategies for reducing emissions.

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.