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Assessing Asian Economies Renewable Energy Consumption Efficiency Using DEA with Undesirable Output

Chia-Nan Wang1, Ngoc-Ai-Thy Nguyen1,*, Thanh-Tuan Dang1,2, Jing-Wein Wang3

1 Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan
2 Department of Logistics and Supply Chain Management, Hong Bang International University, Ho Chi Minh, 723000, Vietnam
3 Institute of Photonics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan

* Corresponding Author: Ngoc-Ai-Thy Nguyen. Email:

Computer Systems Science and Engineering 2022, 43(1), 347-362.


Along with vast non-fossil potential and significant expertise, there is a question of whether Asian nations are attaining efficient consumption and exploitation of renewable resources. From this perspective, the paper aims to evaluate the efficiency of 14 potential Asia countries in renewable energy consumption during the six-year periods (2014–2019). In analyzing the performance of the renewable energy sector, the data envelopment analysis (DEA) with an undesirable output model approach has been widely utilized to measure the efficiency of peer units compared with the best practice frontier. We consider four inputs and two outputs to a DEA-based efficiency model. Labor force, total energy consumption, share of renewable energy, and total renewable energy capacity are inputs. The outputs consist of CO2 emissions as an undesirable output and gross domestic product as a desirable output. The results show that United Arab Emirates, Saudi Arabia, Japan, and South Korea consistently outperform in the evaluation, achieving perfect efficiency scores during the research period. Uzbekistan is found to have the lowest average efficiency of renewable energy utilization.


Cite This Article

C. Wang, N. Nguyen, T. Dang and J. Wang, "Assessing asian economies renewable energy consumption efficiency using dea with undesirable output," Computer Systems Science and Engineering, vol. 43, no.1, pp. 347–362, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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