Abstract
In order to have a sustainable economic and social development, it is important to balance economic growth and ecological environmental damage. In this article, we used the resampling model under the triangular distribution to evaluate energy efficiency, because the input/output value may have measurement errors, time lag factors, arbitrariness, and other problems, causing their own DMU to change. After these factors were taken into consideration, the resampled input/output was estimated because a super-SBM efficiency value was placed in the confidence interval. From the past-present data, for the estimated data change, the time weight was provided according to the Lucas series, and the super-SBM was time-weighted. We applied this model to a dataset of G20 economies from 2010 to 2014. To the best of our knowledge, very few studies have applied the DEA method with resampling to analyze energy efficiency. Thus, our study contributes to the methodologies for energy efficiency evaluation. We found that the overall average energy efficiency is 0.653, with substantial differences between developed economies and developing economies. The most important finding is that neither overestimation nor underestimation occurred when sampling was repeated one thousand times using 95% and 80% confidence intervals, confirming the robustness of the super-SBM model. The less energy-efficient economies should adjust their energy policies appropriately and develop new clean energy technologies in the future.
Highlights
Climate change has drawn considerable global concern as a growing threat to humanity and ecosystems
Accumulating evidence has strongly suggested that the global climate is changing as a result of human activities, those that cause the release of greenhouse gases from fossil fuels; and given the fact that the world economy is still highly dependent on traditional energy sources, improving the energy efficiency is critical to alleviating global climate change
The input and output data in multiple periods take on a triangular distribution simulation according to the following steps. (I) Super-SBM DEA is used to obtain the efficiency score of each decision-making unit (DMU). (II) Processes (i) and (ii) are repeated for the selected number of times, as follows: (i) the data generation manner is used to generate a set of input and output data, and (ii) the super efficiency score of each DMU is obtained and recorded 1000 times with resampling. (III) Confidence intervals (i.e., 97.5%, 90%, 80%, 75%, 60%, 50%, 40%, 25%, 20%, 10%, and 2.5% confidence intervals) are obtained for each DMU
Summary
Dan Wu 1,2 , Ching-Cheng Lu 3,* , Pao-Yu Tang 4, Miao-Ling Wang 5 and An-Chi Yang 3.
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