Assessing the Potential of AI–ML in Urban Climate Change Adaptation and Sustainable Development

Abstract

This study addresses a notable gap in the climate change literature by examining the potential of artificial intelligence and machine learning (AI–ML) in urban climate change adaptation and sustainable development across major global continents. While much attention has been given to mitigation strategies, this study uniquely delves into the AI–ML’s underexplored role in catalyzing climate change adaptation in contemporary and future urban centers. The research thoroughly explores diverse case studies from Africa, Asia, Australasia, Europe, North America, and South America, utilizing a methodological framework involving six-step and five-step models for systematic literature reviews. The findings underscore AI–ML achievements, illuminate challenges, and emphasize the need for context-specific and collaborative approaches. The findings imply that a one-size-fits-all approach is insufficient. Instead, successful adaptation strategies must be intricately linked to the particular characteristics, vulnerabilities, and intricacies of each region. Furthermore, the research underscores the importance of international collaboration, knowledge sharing, and technology transfer to expedite the integration of AI–ML into climate adaptation strategies globally. The study envisions a promising trajectory for AI–ML in the climate adaptation domain, emphasizing the necessity for ongoing research, innovation, and practical AI–ML applications. As climate change remains a defining challenge, this research predicts an increasingly pivotal role for AI–ML in constructing climate-resilient urban centers and promoting sustainable development. Continuous efforts to advance AI–ML technologies, establish robust policy frameworks, and ensure universal access are crucial for harnessing AI–ML’s transformative capabilities to combat climate change consequences.

Investigating Nonpoint Source and Pollutant Reduction Effects under Future Climate Scenarios: A SWAT-Based Study in a Highland Agricultural Watershed in Korea

“…Our findings are consistent with those of a study that used the HEC-HMS model to identify critical years marked by significant changes in each subbasin [57], unlike this study, identified important years highlighted by considerable changes in each subbasin by using the Mann-Kendall (M-K) method, sliding t-test, and cumulative anomaly test. Another study that used the LISFLOOD model, which normally models bigger areas such as river basins or continents, encountered difficulties in capturing small-scale changes in sediment transport [58].…”

“…Integrating data from TN and TP variations provides for a holistic approach to nutrient management, optimizing environmental conservation efforts. Previous study [57] emphasizes the need to take a comprehensive approach to adapting to changing climatic conditions, and Similar study measures indicating a constant drop in discharge values under comparable climate change scenarios, have validated our findings [59]. However, the effects of various factors on runoff were primarily concentrated during the high-flow period of the year.…”

Towards Carbon-Free Automotive Futures: Leveraging AI And ML For Sustainable Transformation

“…Advanced machine learning (ML) techniques such as deep learning (DL) and anomaly detection are used to monitor data from various sensors and predict any potential mechanical issues [22]. This ML-based proactive monitoring helps repair/rectify mechanical issues before they escalate into major failures, which helps reduce energy consumption and emissions and increases safety [24].…”

“…Policy Initiative, are communicating the necessity of regulations in promoting A.I. use within the automotive industry [24,27]. Continued updates and governance are therefore crucial.…”