Artificial intelligence and machine learning are transforming scientific disciplines, but their full potential for climate change mitigation remains elusive. Here, we conduct a systematic review of applied machine learning studies that are of relevance for climate change mitigation, focusing specifically on the fields of remote sensing, urban transportation, and buildings. The relevant body of literature spans twenty years and is growing exponentially. We show that the emergence of big data and machine learning methods enables climate solution research to overcome generic recommendations and provide policy solutions at urban, street, building and household scale, adapted to specific contexts, but scalable to global mitigation potentials. We suggest a meta-algorithmic architecture and framework for using machine learning to optimize urban planning for accelerating, improving and transforming urban infrastructure provision.

人工智能和机器学习正在改变科学学科，但是它们在缓解气候变化方面的全部潜力仍然难以捉摸。在这里，我们对与减缓气候变化相关的应用机器学习研究进行了系统的综述，重点是遥感，城市交通和建筑领域。相关的文学作品跨越了二十年，并且呈指数增长。我们表明，大数据和机器学习方法的出现使气候解决方案研究能够克服通用建议，并在城市，街道，建筑物和家庭规模提供政策解决方案，以适应特定情况，但可扩展到全球缓解潜力。