An Analysis On Energy Efficiency Of China’s Steel Industry Based On SBM Model

China is a country of great steel production, consumption, import and export, and it has leapt to the world’s largest steel-producing countries. The steel industry plays an important role in the national economy, and in2011the industrial output value of the steel industry constituted10%of the country’s total industrial output value, and the steel industry provides an important raw material protection for the construction, machinery, automobiles, home appliances, shipbuilding and other industries as well as the rapid development of the national economy. However, there is a high input, high consumption, high emission, high pollution and other problems in China’s steel industry, resulting in a lot of waste of resources, but also generated a lot of greenhouse gases, and destructing and polluting the environment. Researching the energy efficiency of steel industry issues can help solve these problems, and this paper is in this context, based on the SBM-DEA model, anglicizing China’s steel industry energy efficiency. Economic energy efficiency is calculated in two ways: First, total factor energy efficiency, two single-factor energy efficiency. Total factor energy efficiency is closer to the reality of the economic system, and the calculation process is relatively complex. We use the total factor energy efficiency to study this problem.DEA model has the unique advantages in evaluating the multi-input and multi-output indicators of the socio-economic system, and it is based on mathematical programming ideas, through the establishment of a linear programming model to evaluate the relative efficiency of decision making units. Using DEA method to measure the efficiency and productivity of inter-unit problem has been proven to be quite effective tool, but as Chames, etc.(1978) had described, DEA evaluation thinking of the relative efficiency requires output must be expand as much as possible while input must be reduced as much as possible. But in reality, the production process is not the case, and some of the production process is with a clear byproduct called "undesirable output." It must minimize these undesirable outputs to achieve the best economic efficiency, but the traditional DEA model make it only increased, and thus contrary to the efficiency evaluation in mind. Most traditional DEA model belong to radial and angular measure, and therefore can not fully take into account the relaxation of the input-output problem, and thus the measure value of the efficiency is inaccurate or biased. However, these studies used by DEA model in the estimation of environmental efficiency do not consider the relaxation type of input and output and, thus ignoring economic inefficiency problem caused by input too much and lack of the desired output of the different decision-making unit, and can not comprehensively measure and compare the environmental and economic efficiency of the decision-making unit. Tone (2003) proposed to deal directly with the relaxation of SBM model (Slacks-based model) is a better solution to this problem. SBM model differs with traditional CCR and BCC model that in SBM model it put the slack variables directly into the objective function, and this not only solves the settlement of the input-output relaxation problem, it also solves the efficiency evaluation in the presence of undesirable output. In addition, SBM model belongs to the non-radial DEA model and non-angle measurement method, and it avoids the biased and influence caused by the selection of the radial and angular deviations, and it can better reflect the essence of efficiency evaluation than any other model. Therefore, this paper used SBM model, select the labor, capital, energy consumption as inputs variables, and select industrial output value, product yield, carbon dioxide emissions as output variables, and estimates the steel industry energy efficiency of China’s29provinces, autonomous regions and municipalities from1990to2011.In the specific analysis process, we used Shi Hong Liang, Chen Kai (2012) method, divided the29regions in accordance with the economic, political, geographical and other factors into seven economic zones. Conclusion of the study showed that in the economically developed Yangtze River Delta and the Bohai Rim Economic Zone, Beijing, Shanghai, the efficiency value is always1, Tianjin, Jiangsu, Guangdong in most years the efficiency value is1, and in the production of the frontier, the economy in relatively backward the northwest, the Southwest region, Qinghai, Hainan’s total factor energy efficiency in most years, the efficiency value is1. In total factor productivity frontier, while the economy is relatively backward economic zone the rest of the provinces in the northwest, to the Southwest Economic Area, the six central provinces, moderate level of economic development of the Northeast Economic Zone to the Pearl River Delta economic zone, total factor energy efficiency performance has a gradual increasing trend. This shows that in various regions of the iron and steel industry, total factor energy efficiency and the level of economic development present a "U-type" relationship. Finally, this paper has decomposed the comprehensive technical efficiency into pure technical efficiency and scale efficiency, and we expect to find invalid reasons from the technical and scale perspective. Conclusions show that the value of energy efficiency is well influenced by the impact of technology, but also by the impact of returns to scale. In the case of the pure technical efficiency equal to1, the returns to scale in economically backward regions should increase; while the economy is relatively developed areas should be decreasing. According to empirical analysis, we propose the final policy recommendations: to promote the capacity adjustments of various areas, eliminate backward production capacity of the steel industry, and promote structural optimization and adjustment of the steel industry and encourage technological innovation.