Measuring China’s Energy Efficiency with Different DEA Models
Measuring China’s Energy Efficiency with Different DEA Models
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DOI：
10.1109/ieem55944.2022.9989706
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Xu Wang;T. Hasuike
中科院分区：
文献类型：
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作者：
Xu Wang;T. Hasuike
本文通过对三种不同类型的数据包络分析（DEA）模型在中国能源效率测度中的应用，对它们进行了评价。DEA在效率测度中的有效性是DEA受到国内外学者广泛关注的主要原因。DEA的主要优点包括它能够为被测决策单元（DMU）提供效率得分和改进目标。改进目标提出了几种改进低效决策单元效率的方法。接近所测量的DMU的改进目标被认为在DEA中容易实现。然而，在以往的研究中，大多数传统的DEA模型用于中国的效率测量提供了一个遥远的改善目标，不能立即实现，需要几年。因此，本研究采用最小距离DEA模型，以提供一个更接近的改善目标。此外，传统的DEA模型和比率型DEA模型的性能进行了研究和比较。本文将这三种DEA模型应用于中国1997年、2002年、2007年和2012年的能源效率测度。本文回顾了三种模型在效率得分和改进目标方面的差异。虽然结果显示出不同的改进目标，但根据实验结果可以推断，降低总体能源消耗和增加GDP仍然是两个有效的措施，低效的省，区，市。
This study evaluates three different types of data envelopment analysis (DEA) models by applying them to measure China’s energy efficiency. The efficacy of DEA in efficiency measurement is the primary reason why DEA has gained significant attentions from researchers across the world. The primary benefits of DEA include its ability to provide both efficiency scores and improvement targets for decision making units (DMUs) under measurement. The improvement targets suggest several ways to improve inefficient DMUs’ efficiency. An improvement target that is close to the DMU under measurement is considered to be easy-to-achieve in DEA. However, in previous studies, most conventional DEA models used for China’s efficiency measurement provided a far improvement targets that cannot be achieved immediately and would require several years. Thus, a least-distance DEA model that can provide a closer improvement target is used in this study. Furthermore, a conventional DEA model and a ratio type DEA model are used to study and compare the performances. All three DEA models are applied to the measurement of China’s energy efficiency in 1997, 2002, 2007, and 2012. The differences in the efficiency scores and improvement targets provided by the three models have been reviewed in this paper. Although the results show different improvement targets, it can be inferred that reducing the overall energy consumption and increasing the GDP are still two effective measures for inefficient provinces, districts, and cities according to the experimental results.