Application and Prospect of Artificial Intelligence Technology in Low-Carbon Cities—From the Perspective of Urban Planning Content and Process

Fengying Yan () and Xinran Qi
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Fengying Yan: School of Architecture, Tianjin University, Tianjin 300072, China
Xinran Qi: School of Architecture, Tianjin University, Tianjin 300072, China

Land, 2024, vol. 13, issue 11, 1-21

Abstract: In the era of digital transformation, artificial intelligence (AI) technology—one of the swiftest growing emerging technologies—when integrated with urban planning, can introduce innovative approaches for low-carbon city development and foster the attainment of dual carbon objectives: carbon neutrality and peak carbon emissions. Current research predominantly investigates the influence and alterations of emerging technologies on urban elements, yet it overlooks a comprehensive examination of the applicable procedures of these technologies and their potential synergy with urban planning. Consequently, this study employs a systematic literature review to delve into the application of AI in sectors such as architecture, transportation, land use, and green space development. It categorizes the specific impact processes into monitoring, identification, simulation, and prediction. By offering an exhaustive analysis of urban planning’s content and methodology, this paper elucidates the role of AI technology in the creation of low-carbon cities. The study found that: (1) Due to the varying degrees of application and integration with professional technologies in different fields, the current research focuses more on architecture, land use, and transportation. (2) Combining the four steps of urban planning, artificial intelligence can be divided into monitoring, recognition, simulation, and prediction types, each with its own characteristics. (3) Overall, AI technology is mainly applied in the identification and simulation of architecture, transportation, and land use. (4) There is still room for improvement in the application of AI technology in waste emissions and other algorithms.

Keywords: artificial intelligence; urban planning; low-carbon; carbon reduction pathway (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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