Abstract
Considering increased global emphasis on energy security, low-carbon economy and environmental governance, the proportion of renewable energy will increase in national power grid systems. Wind power generation will play an important role in China's future power systems. Environmental uncertainty will affect the time-varying correlation between carbon efficiency and the performance of the wind power enterprises. Panel data from 2011 to 2018 is used to analyze the development status and existing problems of the wind power industry; further, with the data, the dynamic conditional correlation coefficient is calculated through the DCC-GARCH model, and the breakpoint analysis method is used to analyze the impact of policy and the economic environment on the time-varying correlations between carbon efficiency and the performance of wind power enterprises. The results show that during the China-US trade war, the sudden trade policy changes that increased tariffs led to a sharp rise in interdependence. The relevant polices inspiring the wind power industry are positively correlated with the dynamic conditional correlation coefficient, while the financial performance of alternative fossil fuels is negatively correlated with the dynamic conditional correlation coefficient.
Highlights
China is currently ranked first in carbon dioxide emissions worldwide
According to the fitting results, the dynamic condition correlation coefficient shows that the average dependence degree is highest in 2018 (0.74) over these 3 years, and the minimum value was 0.59 in 2017
The maximum difference of the dynamic condition correlation coefficient reached 0.6371, and there were some sharp fluctuations at some time nodes
Summary
China is currently ranked first in carbon dioxide emissions worldwide. In 2017, coal consumption accounted for 60.4% of China’s energy consumption, while this number ranges from 5 to 25% for several developed countries. In 2018, the largest grid-connected capacities from wind power were in Shanghai (12%), followed by Hainan (11%) and Shandong (11%), which was related to the recent development of offshore wind power projects. Abandoned wind energy and abandonment rate have always been important factors in recent years and hinders further development of wind power. Both of them are due to the large installed capacity of the power station where the wind power generation is located, which exceeds the local electricity consumption. The abandonment of wind power in Gansu and Xinjiang were similar, with both showing gradual increases in 2014-2017; the highest rate of windfall reached 47%. 6.4579 7.0729 0.837793864 0.701898559 −0.973430091 −0.504312268 4.2174 7.5691 used according to the AIC (Akaike Information Criterion), and the mean equation is ARMA (1,1), which is stated as: EMPIRICAL RESULTS AND DISCUSSION
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