Measuring carbon emission performance in China's energy market: Evidence from improved non-radial directional distance function data envelopment analysis

• A new method for measuring environmental performance is proposed. • Improved non-radial directional distance function data envelopment analysis. • A global non-radial Malmquist-Luenberger productivity index. • Eastern China's power market's environmental performance has improved but is unstable. • Technical and scale efficiency greatly affect power enterprises’ environmental performance. The most complex challenge facing the energy market is identifying effective solutions to reduce CO 2 emissions (CEs) and enhance environmental performance (EP). Coal production within the power sector is the primary source of these emissions. In this study, we developed a novel linear programming model that accounts for undesirable outputs to assess the EP of 15 power enterprises in eastern China from 2016 to 2020. In addition, we employed a global non-radial Malmquist-Luenberger productivity index (GNML) to analyse the mechanisms influencing changes in efficiency among these enterprises. Our findings indicate that, while the EP of the power industry in eastern China improved, it remains at a relatively low level and exhibits instability. Moreover, technological efficiency (TE) and scale efficiency (SE) play a significant role in determining production efficiency within the sector. Therefore, it is essential for industry managers to implement standardized production management regulations, enhance technological development and scale investments, and strengthen control over unintended emissions that could facilitate energy transition.

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Publication Details

Journal
European Journal of Operational Research
Published
2024-11-01
DOI
https://doi.org/10.1016/j.ejor.2024.11.019
Citations
6
Primary Topic
Efficiency Analysis Using DEA
Type
article
Field-Weighted Citation Impact
2.94
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article

Measuring carbon emission performance in China's energy market: Evidence from improved non-radial directional distance function data envelopment analysis

Chaochao Zhang, Muhammad Ali Nasir, Yinghao Pan, Jie Wu
6 citations
European Journal of Operational Research
Efficiency Analysis Using DEA
2.94
article

Measuring carbon emission performance in China's energy market: Evidence from improved non-radial directional distance function data envelopment analysis

Chaochao Zhang, Muhammad Ali Nasir, Yinghao Pan, Jie Wu
article en
6 citations

Abstract

• A new method for measuring environmental performance is proposed. • Improved non-radial directional distance function data envelopment analysis. • A global non-radial Malmquist-Luenberger productivity index. • Eastern China's power market's environmental performance has improved but is unstable. • Technical and scale efficiency greatly affect power enterprises’ environmental performance. The most complex challenge facing the energy market is identifying effective solutions to reduce CO 2 emissions (CEs) and enhance environmental performance (EP). Coal production within the power sector is the primary source of these emissions. In this study, we developed a novel linear programming model that accounts for undesirable outputs to assess the EP of 15 power enterprises in eastern China from 2016 to 2020. In addition, we employed a global non-radial Malmquist-Luenberger productivity index (GNML) to analyse the mechanisms influencing changes in efficiency among these enterprises. Our findings indicate that, while the EP of the power industry in eastern China improved, it remains at a relatively low level and exhibits instability. Moreover, technological efficiency (TE) and scale efficiency (SE) play a significant role in determining production efficiency within the sector. Therefore, it is essential for industry managers to implement standardized production management regulations, enhance technological development and scale investments, and strengthen control over unintended emissions that could facilitate energy transition.

European Journal of Operational Research
University of Science and Technology of China (CN), University of Leeds (GB), University of Cambridge (GB)
Affordable and clean energy
Openalex Percentile: Top 8%
Efficiency Analysis Using DEA
2.94
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