BCC CCR Super-Efficiency DEA Model

This paper aims to analyze the energy usage patterns in China's regional industrial sectors, exploring how non-energy input Data Envelopment Analysis (DEA) models can be applied for industrial energy efficiency evaluation, and subsequently proposing relevant policy recommendations. First, we need to understand the current state of industrial energy consumption in China, including energy types, consumption volumes, and regional distribution patterns. The analysis typically involves data preprocessing steps such as normalizing input-output variables and handling missing values through Python's pandas library or R's data.frame operations. Second, we examine the fundamental theory and application methodology of non-energy input DEA models, particularly focusing on the BCC (Banker-Charnes-Cooper) and CCR (Charnes-Cooper-Rhodes) super-efficiency variants. Key implementation aspects include: - Formulating linear programming problems to calculate efficiency scores - Using optimization libraries like Python's SciPy or specialized DEA packages in R - Handling super-efficiency cases where efficiency values may exceed 1.0 - Incorporating non-energy inputs (labor, capital) alongside energy consumption metrics The discussion covers both advantages (handling multiple inputs/outputs, no need for predefined production functions) and limitations (sensitivity to outlier data, inability to test statistical significance directly) of DEA in industrial energy assessment. Finally, based on the efficiency scores derived from DEA modeling - typically computed through iterative optimization algorithms - we propose targeted policy recommendations to promote sustainable development in China's industrial sector. The policy suggestions are grounded in quantitative efficiency rankings and slack variable analysis from the DEA computations.