Data Science for Social Climate Change Modelling: Emerging Technologies Review

Climate change is one of the most acute global problems, the consequences of which are becoming more and more noticeable every year and are the subject of scientific debate, as well as discussions in business and society. Advanced tools for modeling and forecasting not only the physical indicators of this problem, but also the social, economic, and biological-evolutionary ones are necessary to reduce risks and gain control over the situation. This paper focuses on the social factors of climate change and their modeling and assessment since it is the involvement of society that is crucial in the implementation and enforcement of relevant initiatives, policies, and legislation. A review of methods and tools for such modeling and forecasting will allow us to analyze their evolution and identify the most effective ones in terms of accuracy and resource use. Particular attention is paid to the topic of multimodal data fusion because the problem of climate change in many aspects, including social, is multidimensional and caused by the cumulative effect of many factors. This paper emphasizes the importance of synthesizing climate and social data streams for evidence-based policymaking. The presented frameworks can be extended to broader applications in socio-ecological modeling and decision support systems.