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

Abstract

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.

Keywords

• artificial intelligence
• machine learning
• local governance
• climate change
• sustainability

Yerel iklim yönetişiminde yapay zekâ ve makine öğrenmesi: Karşılaştırmalı bir çoklu-vaka analizi

Abstract

Yapay zekâ ve makine öğrenmesini de kapsayan dijital teknolojilerdeki hızlı gelişmeler, iklim değişikliğiyle mücadele ve yerel kaynakların sürdürülebilir yönetimi açısından yerel yönetimlere yeni yönetsel kapasite alanları sunmaktadır. Nüfus yoğunluğu, karmaşık altyapı sistemleri ve sanayi faaliyetleri nedeniyle iklim değişikliğinin etkileri en yoğun biçimde kentlerde hissedilmekte; ancak kentler ekonomik yapı, sosyoekonomik gelişmişlik düzeyi, coğrafi konum ve kurumsal yönetişim kapasitelerine bağlı olarak bu etkileri farklı biçimlerde deneyimlemektedir. Bu çalışma, Kopenhag, Barselona, Singapur, New York, Rotterdam ve Haydarabat örnekleri üzerinden, yerel yönetimlerin yapay zekâ ve makine öğrenmesi teknolojilerini iklim yönetişiminin temaları arasında yer alan (i) öngörümleyici analitikler ve erken uyarı sistemleri ile (ii) yerel kaynakların optimizasyonu kapsamında nasıl kullandıklarını karşılaştırmalı olarak incelemektedir. Analiz bulguları, incelenen kentlerde yapay zekâ ve makine öğrenmesi teknolojilerinin ağırlıklı olarak taşkın, enerji ve su talebi gibi alanlarda öngörümleyici analitikler ve erken uyarı sistemleri yoluyla iklim risklerinin azaltılmasına yönelik kullanıldığını göstermektedir. Bunun yanı sıra, bu teknolojiler enerji ve su gibi kaynakların daha etkin ve verimli biçimde yönetilmesine yönelik optimizasyon uygulamalarında da kullanılmaktadır. Bulgular ayrıca, yapay zekâ ve makine öğrenmesi kullanımının kentlerin karşı karşıya oldukları özgül iklim risklerine ve kurumsal kapasitelerine bağlı olarak farklılaştığını ve bu teknolojilerin çoğunlukla veri analitikleri, nesnelerin interneti ve benzeri destekleyici dijital altyapılarla birlikte kullanıldığını da ortaya koymaktadır.

Keywords

• yapay zekâ
• makine öğrenmesi
• yerel yönetişim
• iklim değişimi
• sürdürülebilirlik

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