Algorithmic green infrastructure optimisation
Review of artificial intelligence driven approaches for tackling climate change
Bibliographic Data
| ID | 21228375 |
|---|---|
| Authors | Abdulrazzaq Shaamala (0000-0003-1683-4192, Queensland University of Technology), Tan Yigitcanlar (0000-0001-7262-7118, Queensland University of Technology, corresponding author), Alireza Nili (0000-0003-1183-7626, Queensland University of Technology), Dan Nyandega (0000-0002-1172-8430, Queensland University of Technology) |
| Year | 2024 |
| Volume | 101 |
| Pages | 105182 |
| Publication date | 2024-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Cities and Society (JOURNAL) |
| Journal identifiers | ISSN: 2210-6707 • E-ISSN: 2210-6715 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.scs.2024.105182 |
| OpenAlex | W4390646596 |
| Language | EN |
| Citations received | 26 |
| References cited | 118 |
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
Business · Climate change · Economics · Efficient energy use · Energy security · Environmental economics · Environmental planning · Environmental resource management · Renewable energy · Sustainability · Water resources · Water security · Building Energy and Comfort Optimization · Computer Science · Engineering · Environmental Science · Noise Effects and Management · Urban Heat Island Mitigation · Ecology
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Urban planning and IPCC-like city assessments integration for climate-resilient cities
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Unveiling causal dynamics and forecasting of urban carbon emissions in major emitting economies through multisource interaction
Enhancing sustainable urban planning to mitigate urban heat island effects through residential greening
Energy consumption prediction for water-based thermal energy storage systems using an attention-based TCN-LSTM model
Are grand tree planting initiatives meeting expectations in mitigating urban overheating during heat waves
A multi-objective optimization framework for designing residential green space between buildings considering outdoor thermal comfort, indoor daylight and Green View Index
Multi-region models built with machine and deep learning for predicting several heat-related health outcomes
Evaluation of street-scale air pollution using multi-source data fusion
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| Unique citing works | 26 |
|---|---|
| Citations per year | 13 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 25 |