Fundamental problems of the natural and social environment of the White Sea and the catchment: Status and possible changes under different scenarios of climate change and economy.

Filatov, Nikolay N.
The Russian Fund for Basic Research, 18-05-60296

System research on social, environmental and economic processes at the White Sea and in its catchment (White Sea region) as part of Russias Arctic zone is carried out with the principal aim to assess the state of and changes in the economy and the White Sea ecosystem, and the opportunities for improving the conditions for the live of the human population. The main characteristics of the state of the environment, climate change, information resources on the White Sea region available and needed for comprehensive studies of the region are described. Scientific research of the region is carried out to assess the climatic and anthropogenic effects on ecosystems in the catchment, the sea itself, and the social environment. It is demonstrated that the state of the environment in Russian regions depends primarily on the development level of the regions economy, its structure, especially the share of industry in GRP. The condition of the soil cover and vegetation of the region is assessed; and the potential effect of their change on matter fluxes from the catchment to the White Sea is considered based both on data from recent expeditions (the Karelian-Kola region surveyed at the first stage) and on remote sensing of the entire catchment. GIS is being developed with information on quite a number of physical-geographical and ecological parameters: structure and dynamics of forest and mire ecosystems which define organic matter production and transpiration; anthropogenic disturbances of the terrain; type of Quaternary deposits. The GIS for the catchment models the hydrological and biogeocoenotic structure of the territory, and is used as a tool for calculating the components of the water and carbon balance. The total input of terrigenous organic matter to the sea correlates with the spatial structure of biogeocoenoses in its catchment. Our studies have shown that the bedrock and steep slopes bearing no loose deposits on the White Sea coast of the Kola Peninsula often generate no such input, and vegetation is also absent in these areas. We have for the first time obtained spatial information on the structure of ecosystems in the catchment, and on all inflowing streams using a comparable unbiased technique.
The effect of the economy and environment of the life of people has been investigated. The efficiency of regional economies was slowly declining after a rapid growth in 1999-2000, but then, after the 2008-2009 crisis, resumed growth (except for the Komi Republic). Model-based calculations have shown that the reduction in the main types of pollution in spite of economic growth since 2000 has been mainly owing to structural shifts in the economy, with less positive effect from economic upgrade and an even lower contribution from environmental investments. Climate warming in the White Sea catchment has been causing an increase in production indices and the yields of a majority of farmed crops. At the same time, for forestry and some other sectors climate warming means a reduction in the availability of natural resources and affects production volumes. The current situation in fisheries and fishing one of the main occupations for local people is considered. Proceeding from the analysis of data for regions in the White Sea catchment, Russian economic policy and global economic effects, four alternative scenarios have been suggested, the most interesting one apparently being the scenario mainstreaming the development of the innovational and other non-material-intensive sectors of the economy through more active support to northern scientific centers and universities. Approaches have been suggested for systemic studies of social, ecological and economic processes in the White Sea region, and the foundations of a cognitive model of the White Sea region have been developed. The first experience of implementing cognitive models in the White Sea region was reported by Menshutkin et al. (2018). This model within RFBR project 18-05-6029618 incorporates over 20 variables representing the economy, population, state of marine ecosystems, such as phytoplankton production, fish harvests, environmental legislation, pollution, climatic conditions, fishing as an essential livelihood for local people. The mathematical fuzzy logic toolkit is used (Zade, 1976). The cognitive model being developed rests upon the interplay and interdependence of the industry, ecological processes, human environment and integrates the economy, natural environment and social processes. The management unit in this model is not an administrative region but rather the regional ecological-economic system. The cognitive model for the White Sea region is made up of four parts: climatic, ecosystem-related, socio-economic, and managerial. The task of the managerial part of the model is to work out such a scheme for the distribution of investments among administrative regions that would ensure the best possible development of all the regions with the given amount of investments. A new version of the cognitive model is currently being developed, which demonstrates that enhancement of the living standard of the population requires capacity-building in fisheries, including improvement of the state and productivity of marine ecosystems, investments in the fisheries industry, favorable legislation (quotas and licensing), environmental stewardship. Analysis of available data shows that marine harvests have declined for almost all fishery targets, and the reason for this decline is not a reduction in stocks, but a general degradation of the economic situation upon the transition to new economic modes. Investments in fisheries need to be enlarged significantly; economic and social relationships need to be improved to restore local fishing and trapping and motivate local people to return to the White Sea. Preliminary calculations in the cognitive fisheries development model were run for different variants of change in the socio-economic situation.
Last modified: June 8, 2019