Temporal and Spatial Evolution,Prediction and Influencing Factors of Industrial Power Consumption Efficiency in Anhui Province
Based on data from Anhui Province from 2013 to 2021,the super efficiency SBM model was used to calculate the industrial electricity efficiency in Anhui Province,and spatial heterogeneity analysis was conducted on the results,providing a certain basis for improving the industrial electricity efficiency of various cities.The overall industrial electricity efficiency in Anhui Province is on the rise,but the industrial electricity efficiency in each city is uneven and needs to be improved.Hefei,Bozhou,Wuhu and Mount Huangshan have high efficiency,while Huaibei,Suzhou,Lu'an,Xuancheng and Chizhou have low efficiency.The grey prediction model GM(1,1)was used to calculate the industrial electricity efficiency values of Anhui Province in the next five periods,and the model accuracy passed the test,indicating high accuracy.This provides a reference for predicting the future development trend of industrial electricity consumption in Anhui Province and creating green and low-carbon industries.Finally,this article conducts a grey correlation analysis on thirteen factors that may affect the efficiency of industrial electricity consumption in Anhui Province from four aspects.The results indicate that factors such as urbanization progress,government financial expenditure,and per capita electricity consumption are highly correlated.There is still room for improvement in industrial technology optimization,in order to better promote the improvement of industrial electricity efficiency.Propose suggestions to continue promoting the government's leadership position in energy conservation,and leverage the government's overall coordination capabilities.Furthermore,increase the energy-saving influence of enterprise technology optimization,make up for deficiencies,and fundamentally improve electricity efficiency.Due to the inability to directly measure the industrial electricity efficiency in Anhui Province and instead using the average of the sum of efficiency in each city as a substitute,there is a certain degree of error in the results.Subsequent research can consider assigning weights to each city based on reasonable indicators to make the results more accurate.
super efficient SBM modelindustrial electricity efficiency in Anhui Provincegrey prediction model GM(1,1)grey correlation analysis
NETL
NSTL
万方数据
