Investigating the sustainability of renewable energy – An empirical analysis of European Union countries using a hybrid of projection pursuit fuzzy clustering model and accelerated genetic algorithm based on real coding.

Renewable energy sustainability plays a significant role in ensuring energy security, improving environment and promoting sustainable development of economic and social. This paper focuses on assessing and analyzing the sustainability and influencing factors of 27 EU countries' renewable energy. By DPSIR-Four-Dimensional indicators system, four sustainability dimensions of energy, economy, society and environment are considered and integrated. To deal with the randomness and fuzziness of multi-dimensional metadata, and to conduct assessment without standard, we develop a nonlinear multi-factor assessment model (projection pursuit fuzzy clustering model with accelerated genetic algorithm based on real coding). The results show that the CO 2 emissions, energy productivity, non-renewable energy emission intensity, energy dependence, electricity price and policy support are closer related to the renewable energy sustainability. The strongest sustainability countries are Denmark and Sweden. From the overall analysis, time-series analysis shows that general sustainable level is a wave of growth. The geographical analysis shows that the countries with better sustainable development are mainly located in the central region, showing a belt distribution from north to south. The Sustainability decreases from the middle to the sides. Image 1 • Applying a composite assessment model to analyze sustainability of renewable energy in 27 EU countries. • The model includes DPSIR, RAGA, projection pursuit and fuzzy clustering iterative analysis. • The model integrates energy, economy, society and environment aspects. • Denmark and Sweden rank highest in the renewable energy sustainability of EU. • The main influencing factors are CO 2 emissions, energy productivity and energy dependence. [ABSTRACT FROM AUTHOR]