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Green infrastructure (GI) is a fundamental building block of our cities. It contributes to the sustainability and vitality of cities by offering various benefits such as greening, cooling, water, air quality, and managing carbon emissions. GI plays an essential role in enhancing overall well-being. The utilisation of artificial intelligence (AI) technologies for GI optimisation is perceived as a powerful approach for cities. A knowledge gap, nevertheless, remains in research on AI-driven GI optimisation for tackling climate change. This study aims to consolidate the comprehension of AI-driven GI optimisation, particularly for tackling climate change. The study methodology adopts the PRISMA protocol to perform a systematic literature review. The review results are analysed from six aspects—i.e., optimisation objectives, objectives categories, indicators, models, GI types, and scales. The findings revealed: (a) GI optimisation was mainly undertaken in the areas of air quality, biodiversity and ecosystem security, energy efficiency, public health, heat islands, and water management; (b) Indicator categories were mainly concentrated on indicators related to GI, indicators related to the objective, and other general/supporting indicators. Based on these findings, a framework was developed to enhance the understanding of the AI-driven GI optimisation process within the realm of climate change.
Shaamala et al. (2024) studied this question.