For several decades, the world economy has shown constant growth, mainly due to the development of industry and the increase in the use of fossil fuels. This case led to a sharp increase in greenhouse gas emissions, which caused almost irreversible climate change. The global scientific community recognizes the anthropogenic causes of such changes and their results – the increasingly frequent natural disasters and catastrophes, such as floods, landslides, hurricanes and fires. Some experts also explain the emergence of new deadly viruses and the spread of dangerous pests for the same reasons. Russia is among the regions most affected by the effects of climate change. So, in the northern latitudes, warming occurs two times faster than in other parts of the world, which causes the melting of permafrost, which results not only in forest fires (the spread and strength of which is 2019 and 2020 broke all previous records) and floods but also manmade disasters (for example, in Norilsk in May 2020, when 21 tons of fuel got into the soil and rivers). This means that urgent measures must be taken to mitigate and adapt to climate change. This article is devoted to analysing the potential that modern digital technologies have in solving this problem. The author examines the Russian and foreign experience of using various tools for data collection and processing, information modeling and communication to predict possible negative phenomena, including a variety of natural disasters and cataclysms, rapid response to them, including measures to save people and limit the spread of such phenomena (for example, extinguishing forest fires), monitoring their development, identifying and eliminating the consequences. Based on the analysis results, the author concludes that Russia does not have a comprehensive approach to solving problems related to climate change using digital technologies and formulates proposals for the inclusion of relevant measures in existing national projects.
climate change; digital technologies; global warming; wildfires; big data; information modeling.