Super-SBM DEA and DTW-based analysis of the energy-environmental efficiency in emerging economies

ABSTRACT

Achieving high economic growth rates has always been the primary objective of emerging economies. While some countries have experienced phenomenal economic success in recent years, there is widespread consensus that economic prosperity has been accompanied by rapid environmental degradation. This research aims to empirically investigate whether the development process in emerging economies were environmentally efficient. The study computes and analyses the energy-environmental super-efficiency scores for 14 leading emerging economies from 1980 to 2019 using the Slack-Based Measure Data Envelopment Analysis with undesirable output. The study also conducts a similarity analysis using the Dynamic Time Warping non-parametric approach, while the Dynamic Time Warping Barycenter Averaging-k-means algorithm is performed to assign economies to different clusters according to their energy-environmental super-efficiency. The findings divulge some divergence regarding the magnitude and evolution over time of super-efficiency scores. Four emerging economies, Brazil, The Philippines, Pakistan, and Vietnam, have been the most efficient, while South Africa recorded the worst scores during the same period. The Dynamic Time Warping path analysis suggests the presence of lead-lag relationships between the super-efficiency scores of China, as the reference economy, and the other economies. Finally, the Dynamic Time Warping Barycenter Averaging-k-means algorithm suggests the presence of four clusters.

KEYWORDS:

• Super-efficiency
• data envelopment analysis
• Slack-Based Measure
• Dynamic Time Warping
• emerging countries

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data supporting the findings of this study are available from the corresponding author.

Additional information

Funding

The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number “IF_2020_NBU_310”.