4.6 Article Proceedings Paper

Energy efficiency evaluation model based on DEA-SBM-Malmquist index

Journal

ENERGY REPORTS
Volume 7, Issue -, Pages 397-409

Publisher

ELSEVIER
DOI: 10.1016/j.egyr.2021.10.020

Keywords

Malmquist index; DEA-SBM model; Total-factor energy efficiency

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This paper analyzes the energy efficiency of 23 cities by evaluating Total Factor Energy Efficiency (TFEE). The results show significant differences in TFEE changes among regions, with cities like Harbin, Shanghai, and Changsha showing an upward trend while Chengdu, Chongqing, and others have minor changes. Inadequate resource allocation and lack of technological innovation are the main reasons for the decline in Total Factor Productivity (TFP).
Energy efficiency represents the level of energy use. By evaluating the level of Total Factor Energy Efficiency (TFEE), we can more accurately analyze the ways to improve energy efficiency and provide suggestions for green energy conservation construction. From the perspective of TFEE, this paper takes capital stock, working population and total energy consumption as input indicators, GDP as expected output, and industrial sulfur dioxide, soot, wastewater discharge and PM2.5 as non-expected output. Select the panel data of 23 cities from 2012 to 2018, calculate the TFEE of each city by using DEA-SBM (Slack-Based Measurement) model and Malmquist index method, and decompose the total factor productivity into comprehensive technical efficiency change and technical progress. The results show that the change trend of TFEE in each region varies greatly during the sample period. The overall TFEE of Harbin and Shanghai shows a continuous upward trend. TFEE in Chengdu and Chongqing has little change and is basically in a state of continuous fluctuation. TFEE of Changsha is the first of the 23 cities studied in this paper, and the resource allocation in this area is more reasonable. TFEE of Shenzhen area has a trend of coming from behind, and the development of this area has made great progress. The lower ranking is Guizhou and Xiamen these two regions. The overall efficiency of the 23 cities selected in this paper shows a downward trend in the survey year, with an average decrease of about 11.2%. From the perspective of sub indicators, the main reason for the decline of TFP is the lack of technological innovation. The decline rate reached 12% in the investigation year, while other indicators basically showed a stable fluctuation state, generally around 1. In general, the development state of TFEE in most regions of China is poor and the absolute value level is too low, which indicates that there is a large space for improvement in resource allocation in each region. (C) 2021 The Author(s). Published by Elsevier Ltd.

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