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The aim of presented study was to identify the most promising new business models which could help to reach climate and energy targets. ‘Business model’ means new opportunities (e.g. business for profit or non-profit community collaboration models) enabled by various technologies in energy domains, i.e., heat and power supply and demand as well as mobility. Based on scientific publications, nine most important technologies and 37 new business models, which could be among the most important for sustainable energy transition, were identified. Mapping of the new business models was done by looking at synergies between the technologies and the energy domains. Valuation of the business models is done by finding ‘expected impact’ with regards to reduction of greenhouse gas (GHG) emissions, which is obtained by multiplication of two factors: ‘potential’ and ‘feasibility’. The ’potential’ represents ability to reduce GHG emissions considering technical characteristics of technologies involved and scalability. The ‘feasibility’ indicates how realistic is implementation of the new business model in the near to mid-term. Experts in the field of energy and environmental engineering were interviewed to obtain scores for the ‘potential’ and the ‘feasibility’ for all business models. The results show that electric mobility is among the solutions with the largest expected impact for reduction of GHG emissions. Results of this valuation will be used to choose the most promising solutions for further analysis with system dynamic modelling.

Implementation of bioeconomy has become an important topic on the national and global scale. Bioeconomy and its development is considered to be affected by many factors, for example, climate change, production, technology, waste and so on. Even more, there are linkages between these factors which, when discovered, could help to achieve one of the bioeconomy’s goals – sustainable use of bioresources. This paper analysis the interlinkage between two bioeconomy affecting factors – climate change and production. It was discovered that between two chosen factors there are seven common indicators: greenhouse gas emissions, investments in energy efficiency, energy consumption, waste generated, resource productivity, created wastewater, and use of freshwater. Regression method was used to conduct the strongest links between these common indicators. Results showed that the strongest links are between the indicators: resource productivity and GHG emissions, energy consumption and waste generated, and resource productivity and waste generated.