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The problems of preserving the natural environment are relevant for unique natural complexes of territories, such as the Baikal region (Russia). There are few studies devoted to forecasting the green economic development of the eco-socio-economic system of the Baikal region. The previous studies are mainly dedicated to the theory of forming the green economy in the Baikal region. This study aimed to forecast and determine future trends in the Baikal region's eco-socio-economic development based on the green economy. The methodological approach to assess the Baikal region development prospects is based on determining the factors affecting the relationship between various eco-socio-economic components using multiple regression models. We constructed the forecast using the artificial neural networks method, and we assessed the possible impact of anthropogenic factors on the Baikal region's eco-socio-economic system in the future. Based on the obtained forecast, we proposed a mechanism for regulating the green economy formation in the region. The regional governments of the Baikal region should perform the management function. This requires establishing a special interregional Green Development Fund to restructure incoming public funds by redistributing financial flows from contaminating brown to green industries and develop a state program for the Green Development of the Baikal region. The constructed forecast is informational support for decision making for the long-term development of the economy during the transition to the green economy, the development and justification of program activities, and the future's main strategic priorities. [ABSTRACT FROM AUTHOR]

In the counties with rapid economy and carbon emissions (CEs) growth, CEs embodied in interprovincial trade (CEs-PT) significantly impacts the CEs amount and structure and represents a key issue to consider in CEs reduction policies formulation. This study applied EEBT and two-stage SDA model to analyze the characteristics and driving force of spatial-temporal evolution for net CEs-PT outflow in the Northeast Industrial District of China (NID). We found that, during 1997-2007, the net CEs-PT flowed out from NID to 16 south and east provinces, then to 23 provinces all over China, and its amount has increased 216.798Mt (by 211.67% per year). The main driving forces are technology and demand (further decomposed into structure and scale matrix); the contribution are 71.6418 Mt and 145.1562 Mt. Then, we constructed coupling relationship model and took the top three industries with the greatest net CEs-PT outflow (farming, forestry, animal husbandry and fisheries, electricity and heat production and supply, petroleum processing, coking, and nuclear fuel processing) as examples, adjusted the interprovincial trade constructions, scales, and objects, to reduce the CEs-PT with lower costs, greater effect, and more equitability. The achievement could provide reference for formulating CEs reduction policies for similar areas in the world characterized by rapid growth of economy and CEs.