A data envelopment analysis model integrated with portfolio theory for energy mix adjustment: Evidence in the power industry

Ximei Zeng, Zhongbao Zhou, Yeming Gong () and Wenbin Liu
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Ximei Zeng: HNU - Hunan University [Changsha]
Zhongbao Zhou: HNU - Hunan University [Changsha]
Yeming Gong: EM - EMLyon Business School
Wenbin Liu: HNU - Hunan University [Changsha], Kent Business School, University of Kent

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Abstract: A reasonable energy mix would effectively improve resource utilization and reduce environmental impact. Decision makers are concerned not only with the current energy efficiency but also with the adjustment of energy mix. However, previous studies mostly focus on energy efficiency and seldom analyse energy mix adjustment. To fill in this literature gap, we address the research question: How to adjust the current energy mix to improve resource utilization and reduce emission? To answer this research question, based on portfolio theory, we first describe the return and risk of energy technologies and build the energy portfolio. Then, we propose a novel data envelopment analysis (DEA) model to measure the energy efficiency and adjust the energy mix. Furthermore, according to the method of improving the DEA frontier, we obtain the optimal energy mix adjustment plan. Moreover, we consider energy technical constraints caused by resource scarcity or geographical conditions while searching for the benchmark. Finally, we apply the proposed methods to the power industry. Interestingly, we find that after adjusting the energy mix, the proportion of renewable energies can be greatly increased and the carbon emission can be significantly reduced on the premise of not increasing other inputs. By our methods, the proportion of renewable energy can rise from 29.40% to 55.53%, and the proportion of coal can decrease from 64.59% to 26.15%. Meanwhile, the average carbon emission per unit of energy mixes can drop from 0.86kg/kWh to 0.65kg/kWh, which will reduce the carbon emissions of the power industry by 24.54%.

Keywords: Data Science; Energy; DEA (search for similar items in EconPapers)
Date: 2022-10-01
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Citations: View citations in EconPapers (1)

Published in Socio-Economic Planning Sciences, 2022, 83, ⟨10.1016/j.seps.2022.101332⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04325619

DOI: 10.1016/j.seps.2022.101332

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