A Study of Total-Factor Energy Efficiency for Regional Sustainable Development in China: An Application of Bootstrapped DEA and Clustering Approach

Abstract

Total-factor energy efficiency (TFEE) is widely used to measure energy efficiency under the data envelopment analysis (DEA) framework, but the efficiencies obtained from DEA are structurally biased upward, and thus TFEE tends to overestimate energy efficiency. This research thus applies the bootstrapped DEA approach to correct the bias of TFEE. Using a dataset consisting of 30 provinces of China in the period 2016–2019, the bootstrapped-based test supports technology with variable returns to scale. The biased-corrected TFEE also indicates that energy consumption on average can be scaled down by 42.36%, rather than the biased value of 19.4%. The bootstrapped clustering partitions provinces into three groups: Cluster 1, with Guizhou as the representative medoid, includes half of the superior coastal provinces in terms of actual energy consumption and TFEE and half of the competitive inland provinces, whereas Cluster 3 outperforms Cluster 2 in terms of TFEE, but the actual energy consumption is higher, with Shandong and Hebei as the representative medoids, respectively. Lastly, empirical results imply that the northeast and central regions need more government attention and resources to practice sustainable development and improve TFEE.

Impact of Intelligent Manufacturing on Total-Factor Energy Efficiency: Mechanism and Improvement Path

“…However, with the deepening of research, there are some defects in the SFEE index; that is, only the energy input factor, capital, labor and other factors have not been included, while the TFEE index can greatly compensate for the impact of energy consumption, labor, capital and other factors on output. Li et al [18] calculated the TFEE of each region in China by comparing the single-factor and the total-factor method, pointing out that the total-factor method has the advantage that the single-factor method cannot replace in evaluating the impact of the regional factor endowment structure on its TFEE. The measurement of TFEE is mainly based on the DEA method in the non-parametric estimation method.…”

The Harmonious Relationship between Energy Utilization Efficiency and Industrial Structure Development under Carbon Emission Constraints: Measurement, Quantification, and Identification

“…Among them, the data envelopment analysis model, as a non-parametric model, is widely used in the field of resource and energy efficiency measurement and has produced rich research results. Relevant studies cover different research scales, such as countries [11][12][13], river basins [14,15], provinces [16,17], and cities [18,19]. For example, Zhang et al used a three-stage SBM model to evaluate the energy utilization efficiency of RECP countries and its influencing factors [12]; Pan et al quantitatively calculated the energy utilization efficiency of 30 provincial-level administrative regions in China based on the SBM-DEA model and explored its driving factors using a regression model [20].…”

“…A comparison of selected studies related to energy efficiency based on data envelopment analysis is shown in Table 1. [11] 25 countries SBM model and Malmquist Index Zhang and Chen (2022) [12] 13 RECP countries Three-stage SBM model Jebali et al (2017) [13] Mediterranean countries Two-stage bootstrap DEA model Ren et al (2020) [14] 30 provinces, China Metafrontier DEA model Ma and Wang (2022) [15] 96 cities, China Super-EBM model Zhao et al (2019) [16] 30 provinces, China Three-stage DEA model Li et al (2022) [17] 30 provinces, China Bootstrapped DEA model Wang and Wang (2020) [18] 284 cities, China Malmquist-Luenberger index Keirstead (2013) [19] 198 cities, UK DEA and Regression model Pan et al (2020) [20] 30 provinces, China SBM and Regression model Eguchi et al (2021) [21] Thermal power plant Multi-hierarchy DEA model Aldieri et al (2021) [22] American companies DEA model and Malmquist Index Ślusarz et al (2021) [24] Provinces of Poland CCR-DEA model Chen et al (2021) [25] 30 provinces, China Mixed integer DEA model Meng and Qu (2022) [26] 29 provinces, China Super-SBM and GML model…”

Sustainable Development Efficiency of Cultural Landscape Heritage in Urban Fringe Based on GIS-DEA-MI, a Case Study of Wuhan, China

“…At present, there have been many studies about applying DEA to efficiency studies, which therefore formed relatively normative research methods and ideas. For instance, Yang Li et al studied the total factor energy efficiency of sustainable development in China based on DEA [ 36 ]. Zhu, S., Zhou, Z., Li, R. and Li, W. used the city-level panel data of the three urban agglomerations from 2006 to 2019 to construct the slacks-based measure integrating data envelopment (SBM-DEA) model for calculating each city’s carbon dioxide emission efficiency [ 37 ].…”