An EEMD-based multi-scale fuzzy entropy approach for complexity analysis in clean energy markets

To measure the efficiency of clean energy markets, a multi-scale complexity analysis approach is proposed. Due to the coexisting characteristics of clean energy markets, the “divide and conquer” strategy is introduced to provide a more comprehensive complexity analysis framework for both overall dynamics and hidden features (in different time scales), and to identify the leading factors contributing to the complexity. In the proposed approach, ensemble empirical mode decomposition (EEMD), a competitive multi-scale analysis tool, is first implemented to capture meaningful features hidden in the original market system. Second, fuzzy entropy, an effective complexity measurement, is employed to analyze both the whole system and inner features. In empirical analysis, the nuclear energy and hydropower markets in China and US are investigated, and some interesting results are obtained. For overall dynamics, the US clean energy markets appear a significantly higher complexity level than China's markets, implying market maturity and efficiency of US clean energy relative to China. For inner features, similar features (in terms of similar time scales) in different markets present similar complexity levels. For different inner features, there are some distinct differences in clean energy markets between US and China. China's markets are mainly driven by upward long-term trends with a low-level complexity, while short-term fluctuations with high-level complexity are the leading features for the US markets. All these results demonstrate that the proposed EEMD-based multi-scale fuzzy entropy approach can provide a new analysis tool to understand the complexity of clean energy markets.