Dynamic Ecology-Economy Interactions Modeling Some Experience and Perspectives of Application in Russian and German Context *)

Solution of environmental problems on global and regional levels urges creation of adequate approaches to study the development of the regional economy and its interaction with the natural environment. For this purpose a generalized input-output model is proposed, which includes: block of fixed assets dynamics; several kinds of linear and nonlinear production functions; algorithms for balanced forecasting of technological structures; nature block describing dynamics of the main environmental indicators (such as: air and water pollution, state of forests and soil, mineral and bio-resources etc.) with self- and artificial restoration, economic and anthropogenic influences taken into account. Nature-Economy Simulation SYstem (NESSY) is used as a basic software tool for building and investigating such models. Application of the system to modeling scenarios of environmental -economic development of Russia is described. The model was built for 13 economic sectors and 8 basic natural resources. Three groups of scenarios were developed to illustrate business as usual, stabilization and “sustainable” future dynamics. These strategies were modeled by different investment policies, and, in particular, by proper distribution of funds for the purposes of nature restoration. It was demonstrated that development, which is both economically efficient, and environmentally sound, can be possible only with such an investment policy that provides considerable share of expenses for new technologies, and namely for low materials consuming and energy-saving ones. A perspective of building a version of the nature-economy interactions model in German context is also discussed. First experience is considered where the analysis of air pollution dynamics is undertaken with the help of a non-linear dynamic input-output model, built on available statistical data for 58 economic sectors of Western Germany. The data was aggregated to 17 sectors with detailed representation of power industry sectors. Major air pollutants under consideration (both of industrial and households origin) were as follows: carbon dioxide, carbon monoxide, dioxide, nitrogen oxides, and volatile organic compounds. Production functions along with equations of both gross and air-cleaning fixed assets dynamics were estimated for each aggregated sector. Changes in emission coefficients for sectors were modeled as non-linear functions of fixed assets for air cleaning. This enables to follow effects of different investment policies on air pollution abatement and to forecast the required steps of technological changes in the ecologically unsound industries. Comparison of scenarios is provided to analyze costs of different environmental-economic development programs.