Electric Power Consumption and Pollutant Emission: A Study Based on DCCA and MF-DCCA

In the context of big data, an in-depth understanding of the potential correlation between electric power consumption and pollutant emission concentration of industrial enterprises is of great significance for effective prediction and monitoring of pollutant emissions. In this paper, considering the properties of nonlinearity and complexity in the data of electric power consumption and pollutant emission of industrial enterprises, we apply cross-correlation statistic test, detrended cross-correlation analysis and multifractal detrended cross-correlation analysis to study the correlation between them. Our numerical example based on an industrial enterprise shows that there is indeed an association between electricity consumption data and pollutant emission concentration of the enterprise. Moreover, the correlations between them demonstrate long memory and multifractality. Our research can help to further predict the pollutant emission using the electric power big data.