National Research university The Higher School of Economics presented a rating of Russian regions according to the level of need to adapt to climate change. The study assesses six major climate risks: thawing permafrost, increased wildfires, prolonged droughts, heat waves, extreme rainfall and flooding, and severe water stress.
Nikolai Kurichev, dean of the Faculty of Geography and Geoinformation Technologies at the National Research University Higher School of Economics, presented a report on October 31 at the TASS news agency. He emphasized that adaptation to climate challenges has become a key factor in the sustainable development of all territories. Russia needs effective tools to integrate the assessment of such risks into the strategic planning system.
The expert noted the interdisciplinary nature of the work carried out. Scientists from various specialties from the university worked on the study. This is explained by the complexity of the problem, which combines natural and socio-economic aspects.
Kurichev explained the importance of correctly setting priorities. This makes it possible to rationally allocate limited resources to solve the most pressing climate problems. It is necessary to clearly understand which regions are most at risk. The vulnerability of territories depends not only on the frequency and intensity of natural hazards, but also on the level of their socio-economic development, as well as the ability to withstand negative changes.
The developed rating helps to assess climate risks for each specific region. Local authorities can use this data to adjust existing adaptation plans, making them more effective and targeted.
The new HSE ranking provides Russian regions with valuable information to prepare for climate challenges. The study helps identify the most vulnerable areas and optimally direct resources to reduce the threats associated with climate change.
The material was prepared with the support of the Russian Ministry of Education and Science as part of the Decade of Science and Technology.
Source: @gis_proxima
Image generated by a neural network
