With the share of electricity in total final energy consumption increasing quickly, the world is becoming increasingly dependent on electricity, which makes it more and more important to improve the forecasting accuracy of electricity consumption to ensure the normal operation of economic activities. In this paper, a novel decomposition and combination technique to forecast monthly electricity consumption is proposed. First, we use STL decomposition to obtain the trend, season, and residual components of the time series. Second, we use SARIMA, SVR, ANN, and LSTM to forecast trend, season, and residual component, respectively. Third, we use time correlation principle to improve the forecasting accuracy of season component. Fourth, we integrated the residual component predicted by SARIMA, SVR, ANN, and LSTM into a new sequence to improve the forecasting accuracy of residual component. In order to verify the performance of the proposed forecast model, monthly electricity consumption data in China is introduced as an example for empirical analysis. The results show that after STL decomposition, time correlation modification, and residual modification, the forecasting accuracy of each model has been gradually improved. We believe that the proposed forecast model in this paper can also be used to solve other mid- and long-term forecasting problems with obvious seasonal characteristics.

Total factor productivity (TFP) is considered a source of economic growth, and as the constraints of climate change and energy security gradually increase, green total factor productivity (GTFP) is also included in the meaning of the topic. In this study, we combine the super-SBM model with the GML index and include undesirable outputs to measure the GTFP of 26 manufacturing sub-sectors in China from 2004 to 2017. The results show that in the sample period, the GTFP growth rate of China’s manufacturing continues to accelerate, and the driving force is mainly from green technological progress change (GTC), while the green technical efficiency change (GEC) generally declines. After entering the 13th Five-Year Plan period, GEC also began to show a growth trend. The acceleration of green technological progress and the improvement of green technical efficiency jointly drove the GTFP of China’s manufacturing industry to enter a rapid upward trajectory during the 13th Five-Year Plan period, with an average annual growth rate of 5.16%. In addition, different categories of China’s manufacturing industry have begun to develop in a different manner in recent years, which is mainly caused by the difference in the growth rate of green technological progress in the manufacturing industry. Specifically, the GTFP growth rate of equipment and high-tech manufacturing is the highest, followed by consumer goods manufacturing and energy-intensive manufacturing. Accordingly, this paper suggests that the manufacturing industry should further increase investment in R&D, optimize resource allocation, and formulate differentiated policies for different industries, so as to improve the GTFP of the manufacturing industry and promote the green and low-carbon transformation of China’s manufacturing industry.

Green technological progress is an important prerequisite for the sustainable development of the manufacturing industry, which is usually closely related to R&D investment activities. This paper explores the nonlinear relationship between R&D investment and green technological progress based on panel threshold regression model using panel data of 26 manufacturing industries in China from 2004 to 2017. The results show that the double-threshold model can better explain the nonlinear relationship between the two, and the R&D investment in the three ranges of low, medium and high levels can significantly promote green technological progress in manufacturing industry. However, with the improvement of R&D investment level, the promotion effect of R&D investment on the progress of manufacturing green technology is decreasing, which explains the low R&D intensity of China's manufacturing industry to a certain extent. When the level of R&D investment reaches a certain level, its promoting effect on green technological progress will be greatly reduced, and the motivation of enterprises to invest in R&D based on self-interest will decrease, so that the scale of R&D investment will be lower than the optimal scale of society. R&D investment can also improve green technical efficiency change. In addition, environmental regulation can promote green technological progress in manufacturing industry. However, due to the implementation of output-oriented environmental regulation policies, China’s environmental regulation can inhibit the improvement of green technical efficiency change. Based on the conclusion, this paper argues that China should implement differentiated R&D subsidy policies for manufacturing enterprises, especially to increase R&D subsidies for enterprises with a medium level of R&D investment, and formulate appropriate environmental regulatory policies, so as to promote green and low-carbon transformation of China's manufacturing sector.