Role of renewable,non-renewable energy consumption and carbon emission in energy efficiency and productivity change:Evidence from G20 economies

The challenge of achieving sustainable economic development with a secure environmental system is a global challenge faced by both developed and developing countries.Energy Efficiency(EE)is crucial in achieving sustainable economic growth while reducing ecological impacts.This research utilizes the Slack-Based Measure Data Envelopment Analysis(SBM-DEA)and the Malmquist-Luenberger Index(MLI)method to evaluate EE and productivity changes from 1995 to 2020 across G20 countries.The study uses four different input-output bundles to gauge the impact of renewable and non-renewable energy consumption and carbon emissions on EE and productivity changes.The study results show that including renewable energy consumption improves the average EE from 0.783 to 0.8578,but energy productivity declines from 1.0064 to 0.9988.Incorporating bad output(carbon emissions)in the estimation process enhances renewable EE and productivity change,resulting in an average EE of 0.6678 and MLI of 1.0044.Technological change is identified as the primary determinant of energy productivity growth in scenarios 1 and 2,while technical efficiency determines energy productivity change in scenarios 3 and 4.The Kruskal-Wallis test reveals a significant statistical difference between the mean EE and MLI scores of G20 countries.

A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies

Data Envelopment Analysis(DEA)is a linear programming methodology for measuring the efficiency of Decision Making Units(DMUs)to improve organizational performance in the private and public sectors.However,if a new DMU needs to be known its efficiency score,the DEA analysis would have to be re-conducted,especially nowadays,datasets from many fields have been growing rapidly in the real world,which will need a huge amount of computation.Following the previous studies,this paper aims to establish a linkage between the DEA method and machine learning(ML)algorithms,and proposes an alternative way that combines DEA with ML(ML-DEA)algorithms to measure and predict the DEA efficiency of DMUs.Four ML-DEA algorithms are discussed,namely DEA-CCR model combined with back-propagation neural network(BPNN-DEA),with genetic algorithm(GA)integrated with back-propagation neural network(GANN-DEA),with support vector machines(SVM-DEA),and with improved support vector machines(ISVM-DEA),respectively.To illustrate the applicability of above models,the performance of Chinese manufacturing listed companies in 2016 is measured,predicted and compared with the DEA efficiency scores obtained by the DEA-CCR model.The empirical results show that the average accuracy of the predicted efficiency of DMUs is about 94%,and the comprehensive performance order of four ML-DEA algorithms ranked from good to poor is GANN-DEA,BPNN-DEA,ISVM-DEA,and SVM-DEA.