Smart City Gnosys
Smart city article details
| Title | Data-Driven Net-Zero Carbon Monitoring: Applications Of Geographic Information Systems, Building Information Modelling, Remote Sensing, And Artificial Intelligence For Sustainable And Resilient Cities |
|---|---|
| ID_Doc | 17460 |
| Authors | Li J.; Shirowzhan S.; Pignatta G.; Sepasgozar S.M.E. |
| Year | 2024 |
| Published | Sustainability (Switzerland), 16, 15 |
| DOI | http://dx.doi.org/10.3390/su16156285 |
| Abstract | NZCCs aim to minimise urban carbon emissions for healthier cities in line with national and international low-carbon targets and Sustainable Development Goals (SDGs). Many countries have recently adopted Net-Zero Carbon City (NZCC) policies and strategies. While there are many studies available on NZCC cities’ definitions and policymaking, currently, research is rare on understanding the role of urban data-driven technologies such as Building Information Modelling (BIM) and Geographic Information Systems (GIS), as well as AI, for achieving the goals of NZCCs in relation to sustainable development goals (SDGs), e.g., SDGs 3, 7,11, 13, and 17. This paper aims to fill this gap by establishing a systematic review and ascertaining the opportunities and barriers of data-driven approaches, analytics, digital technologies, and AI for supporting decision-making and monitoring progress toward achieving NZCC development and policy/strategy development. Two scholarly databases, i.e., Web of Science and Scopus databases, were used to find papers based on our selected relevant keywords. We also conducted a desktop review to explore policies, strategies, and visualisation technologies that are already being used. Our inclusion/exclusion criteria refined our selection to 55 papers, focusing on conceptual and theoretical research. While digital technologies and data analytics are improving and can help in the move from net-zero carbon concepts and theories to practical analysis and the evaluation of cities’ emission levels and in monitoring progress toward reducing carbon, our research shows that these capabilities of digital technologies are not used thoroughly yet to bridge theory and practice. These studies ignore advanced tools like city digital twins and GIS-based spatial analyses. No data, technologies, or platforms are available to track progress towards a NZCC. Artificial Intelligence, big data collection, and analytics are required to predict and monitor the time it takes for each city to achieve net-zero carbon emissions. GIS and BIM can be used to estimate embodied carbon and predict urban development emissions. We found that smart city initiatives and data-driven decision-making approaches are crucial for achieving NZCCs. © 2024 by the authors. |
| Author Keywords | building information modelling (BIM); city digital twins (CDTs); data-driven method; digital technologies; embodied carbon emissions; geographic information systems (GIS); net-zero carbon city (NZCC); sustainability; sustainable development goals (SDGs) |
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