**Author details**

Chenchen Yang, Feng Yang, Liang Liang\* and Xiping Xu *School of Management, University of Science and Technology of China, Hefei, P. R. China* 

<sup>\*</sup> Corresponding Author

## **Acknowledgement**

We would like to thank the financial support by Program for New Century Excellent Talents in University, Ministry of Education of China and National Natural Science Foundation of China (Grant No. 70801056, 71121061, 71090401/71090400 and 71110107024).

#### **6. References**

222 Energy Efficiency – The Innovative Ways for Smart Energy, the Future Towards Modern Utilities

get recovery which makes MPI increasing.

manufacturers at the micro level.

Chenchen Yang, Feng Yang, Liang Liang\*

**Author details** 

Corresponding Author

*P. R. China* 

 \*

**5. Conclusion** 

It is interesting to investigate the individual province. Here are some examples. First, we make a comparison between Shanghai and Hainan both of which are efficient during the periods. However, detailed data indicate that Shanghai keeps making frontier forward gradually while Hainan are opposite except year 2002. This could be explained by that the location of Hainan on the frontier is on the edge. Second, take Beijing for example. Beijing is non-efficient from 2000 to 2002 and finally becomes efficient at 2003 by making efforts on improving technical efficiency and putting frontier forward. Third, energy efficiency of Shandong province suffers a decline and is back to the normal level later. MPI during first period is decreasing mainly caused by declining CE. In the next two years, some parameters

This chapter reviews the development process of the evaluation technique of energy efficiency and focuses on introducing the concept of energy intensity. However, missing the structure of energy consumption causes the energy efficiency estimated inaccurately. Thus, the current chapter introduces a weighed energy efficiency model based on DEA to fix it. Energy cannot produce production without non-energy inputs such as labor and capital. That's why we extend the method to the total factor energy efficiency model. Energy efficiency of China and other 47 countries in 2003 are employed to illustrate the models. Results show that unbalance of energy efficiency exists. For china, specially, it needs to adjust energy consumption structure as its poor energy efficiency and improve GDP since

its total factor energy efficiency is at a lower level than some developed countries.

As a key part, the chapter adopts Malmquist production index technique to analyze the dynamic change in energy efficiency of Chinese provinces which can further explore the reason for the variation of energy efficiency deeply. The chapter uses the proposed models to investigate the changes in energy efficiency of provinces in china during the period of 2000 to 2003. We find that the east area has better energy efficiency than the central and west area but lower improving rapid. In addition, it is interesting to find that energy efficiency of most provinces improves due to the extending frontier. Although our work mainly focuses on estimating energy efficiency at the macro-level, it can provide guidance to managers and

and Xiping Xu

*School of Management, University of Science and Technology of China, Hefei,* 

	- [16] Tone, K., Malmquist productivity index. Handbook on data envelopment analysis, 2004: p. 203-227.

**Chapter 11** 

© 2012 Woo et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

(a) The bathtub curve and straight line with slope

and reproduction in any medium, provided the original work is properly cited.

**The Reliability Design** 

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/48790

straight line with the slope angle

**1. Introduction** 

**and Its Direct Effect on the Energy Efficiency** 

Seong-woo Woo, Jungwan Park, Jongyun Yoon and HongGyu Jeon

β

product life *LB* (or mean time between failures) and failure rate

Reliability refers to the ability of system or component to perform a required function under stated environmental and operational conditions for a specified period of time. Traditionally, the reliability over the product life can be illustrated by a bathtub curve that has three regions: a decreasing rate of failure, a constant rate of failure, and an increasing rate of failure, as shown in Figure 1(a). As the reliability of a product (or part) improves, failure of the part becomes less frequent in the field. The bathtub curve may change into a

. In a straight line there are two variables to be measured:

λ

, as shown in Eq. (1):

β

