Artificial intelligence and machine learning in local climate governance: A comparative multiple-case study

Rapid developments in digital technologies, including artificial intelligence and machine learning, are providing local governments with new managerial capacity areas for combating climate change and ensuring the sustainable management of local resources.Due to population density, complex infrastructure systems, and industrial activities, the impacts of climate change are felt most intensely in urban areas.However, cities experience these impacts differently depending on their economic structures, levels of socioeconomic development, geographical conditions, and institutional governance capacities.This study comparatively examines how local governments use artificial intelligence and machine learning technologies within two thematic dimensions of climate governance: (i) predictive analytics and early warning systems, and (ii) local resource optimization, through the cases of Copenhagen, Barcelona, Singapore, New York, Rotterdam, and Hyderabad.The findings indicate that artificial intelligence and machine learning technologies in the examined cities are primarily used to reduce climate-related risks through predictive analytics and early warning systems in areas such as flooding, energy demand, and water demand.In addition, these technologies are also utilized in optimization applications aimed at managing resources such as energy and water more effectively and efficiently.The findings further reveal that the use of artificial intelligence and machine learning differs according to the specific climate risks and institutional capacities of cities, and that these technologies are generally employed together with complementary digital infrastructures such as data analytics, the Internet of Things, and similar support systems.