| China is a rich country in resource and also a poor country in resource. And the total amount of energy leap into the front ranks of the world, but the per capita possession is behind the world average. Due to the influence of modern history and geographical differences in the structure, it lead to unbalanced development of regional economy, industrial internal structure is relatively backward, total industrial energy consumption is great and utilization efficiency is low. National statistics show China’s industrial energy consumption is total by 16.8 million tons of standard coal in 2005 increased to 25.2 million tons of standard coal in 2012, with an average annual growth rate of 9.6%, more than 70% of industrial energy accounted for the total energy consumption. China’s economic development is at the expense of a large number of resources and energy consumption developed, resulting in China’s energy problems and the environment is becoming increasingly serious. In order to better illustrate China’s industrial energy efficiency and its influencing factors and to improve China’s industrial energy efficiency, energy saving and emission reduction to provide beneficial suggestions and reference, this paper makes relevant theoretical model research from three aspects.First of all, this paper makes an empirical study on the energy efficiency and the influencing factors from the provincial level based on cross-sectional data by DEA model of provincial energy efficiency was calculated. Due to the traditional using linear or log linear parametric regression econometric models may exist multiple co linearity, self and other related issues, this paper introduces the artificial neural network (ANN) to analyze the influence factors of energy efficiency. Empirical results show that:(1) the energy is fully and effectively utilized with the exception of a few provinces, and the remaining provinces are redundant, especially in the central and western regions which the energy saving potential is great; (2) the level of technology, R & D intensity, labor productivity, the degree of nationalization, the degree of market openness have a significant impact on energy efficiency, but industrial structure and industrial enterprises have no significant impact on the average size; (3) sort according to the degree of impact on energy efficiency, the degree of market openness is followed by technology level, R & D intensity, labor productivity, the degree of nationalization.Second, this paper makes an empirical study on the energy efficiency and the influencing factors from the perspective of 37 industrial sectors. DEA has become the main method to calculate the comprehensive utilization of energy efficiency as a non-parametric mathematical programming model. But most of them are based on traditional CCR and BCC model of DEA to measure China’s industrial sector energy efficiency. With the model does not take the influence caused by the elements and output elements relaxation into account, it will make the DEA efficiency deviation. So the paper takes the consideration to the change of input and output of relaxation of super efficiency DEA model to 2005-2011 37 industrial panel data studied industrial energy efficiency utilization, and uses individual point fixed effect model of 37 industrial energy efficiency and its influencing factors of the panel data regression analysis. The results showed that:(1) in addition to a small number of industrial DEA super efficiency, the vast majority of the industry’s energy efficiency is low and industry energy saving and emission reduction potential is very large; (2) the level of technological progress, market openness, energy structure, enterprise size, labor productivity on the overall, high, medium and low energy efficiency of the industry is not the same; (3) the energy structure has the greatest impact on the energy efficiency of the whole, high and medium industries, while the level of technological progress plays an important role in all industries; (4) the level of technological progress, market openness, energy structure, enterprise size, manufacturing has a significant impact on the extractive industries and hydropower gas production and supply industry has no effect.Third, this paper makes an empirical study on the short-term and long-term dynamic relationship between the industrial energy efficiency and the influencing factors. Current research on energy efficiency focuses on the research of single output factor. However, in addition to the total output value of industrial output, there are inevitably accompanied by a non-expected output, such as waste water, waste gas and solid waste. Based on the time series data from 1989 to 2012, this paper studies the change of the overall energy efficiency of China’s industry in 24 years by using the SBM model of the non-expected output:Using VAR impulse response function to analyze the short-term and long-term dynamic relationship between industrial energy efficiency and its influencing factors in china. Research results found:, (1) the overall efficiency of China’s industrial environment presents a trend of increasing; (2) scientific and technological progress, energy price, industrial structure and the dynamic relationship between total factor energy efficiency and long-term positive energy consumption structure, market closure degree and energy efficiency are the negative relationship; (3) the positive impact of R & D intensity on energy efficiency is the most important in the short term.Finally, based on the summary of the full text of the study and according to the conclusions of the study, the paper puts forward the policy recommendations to improve the efficiency of China’s industrial energy use. |
PDF Full Text Request