Improving the development and utilization of renewable energy is one of the critical goals in the green industrial revolution of China. The energy efficiency measurement and the detection of influence factors are the very important issues to accomplish this task. In this paper, feature recognition is applied to determine the factors that affect the energy efficiency. Based on the feature recognition algorithm, data mining methods are used to derive knowledge from a dataset of 24 provinces or cities in China. In the process of data mining, the following three problems are addressed: 1) the optimal feature subset is selected from the original feature set that affects the energy efficiency; 2) the energy efficiency based on the optimal feature subset is evaluated; and 3) the energy efficiency in China is predicted by seven good-fitted classification models, whose accuracy rates are higher than 90%. Combining the results of feature selection and energy efficiency prediction, the strategy and policy to improve the energy efficiency in China is suggested. Green energy policies and prediction institutions of energy efficiency are really necessary in each province to improve their energy consumption structures, especially in coordinating the development between the traditional energy and renewable energy.
In recent years, many western scholars and professional organizations such as SIM conducted numerous studies about the key issues that CIO's face currently or will be facing in the next three to five years. However, such issues have not been the focus in mainland China. The survey conducted recently by Li et al identified the key issues confronting the Chinese CIOs. This paper analyzes those survey results and compares them with other related surveys conducted by the SIM in 2004 and 2005. We concluded that the key issues in China today appear to be similar with those in the U.S. in 1990s. This paper also predicted the trends of the key issues of Information Systems in Mainland China in the next three to five years.
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