Start-Ups and Entrepreneurship in Renewable Energy: Investments and Risks

Advances in renewable energy technologies, particularly in solar, wind, energy storage, and grid integration solutions, are accelerating the growth of in startups in this sector. Despite the promising outlook, renewable energy startups face several risks, including technological, regulatory, financial and market risks. The article carries out a bibliometric analysis of scientific publications related to the problem of investment and risks associated with the development of entrepreneurship and start-ups in the field of renewable energy. After multi-level filtering of the dataset, the research base consists of 82 publications by 232 scientists for 2005-2023, indexed by Scopus, and the analysis tools used are Biblioshiny and Excel. In 2005-2016, the number of publications grew annually at a rate of 10.47%, with peaks in 2006, 2007, and 2016; in 2017-2023, the number of papers increased (the dependence is described by a third-degree polynomial trend). The article examines 3 different types of Three-field plots in the topic of the investments and risks of the startups and entrepreneurship in renewable energy: 1) “References – Authors – Keywords”, 2) “Countries – Institutions – Authors”, 3) “Authors – Sources – Keywords”. A quantitative and qualitative analysis of the scientific journals that make the largest / periodic but noteworthy / minimal contribution to the dissemination of knowledge in this area was carried out, the top 10 authors and papers were analysed by the levels of local and global influence, the most powerful research networks (based on the results of the analysis of joint citations) were identified both in terms of authors and countries, and the leading countries were identified in terms of the volume of research output of their scientists and the intensity of citation of these works. Building clouds of the most used keywords allowed us to identify priority thematic areas of research, as well as to analyse the structural and logical relationships between different thematic blocks in the research landscape. With the help of multiple correspondence analysis (factorial analysis) and longitudinal thematic map analysis, the scientific landscape in this area is clustered and evolutionary changes and interdependence of individual topics are identified.