Analysis of the total factor energy efficiency and its influencing factors of the Belt and Road key regions in China.

Energy cooperation has been emphasized strongly in the Belt and Road (B&R) initiative. Therefore, the energy efficiency of China has attracted much attention from experts. However, relevant studies are still insufficient. This paper analyzes the total factor energy efficiency (TFEE) and its influencing factors of 17 B&R key regions from 2005 to 2015. We use the ratio of target energy input and actual energy input to calculate the regional TFEE under environmental constraints. The Malmquist index and the Tobit model are applied to investigate the internal and external influences of TFEE. Measurement analysis shows that the TFEE of the B&R key regions has not improved in recent years and it is unbalanced during the study period. Regions in the east area have the highest TFEE; regions in the west area have the second high TFEE; and regions in the north area have the lowest TFEE. Regression analysis shows that for the B&R key regions, technical changes, coal consumption, research and development, and environmental pollution have mainly negative effects on TFEE; pure efficiency changes, scale efficiency changes, economic structure, opening up, and government finance have mainly positive effects on TFEE. Finally, precise policy implications are proposed.

Measurement and decomposition of energy efficiency of Northeast China-based on super efficiency DEA model and Malmquist index.

Nowadays, environment problem has become the international hot issue. Experts and scholars pay more and more attention to the energy efficiency. Unlike most studies, which analyze the changes of TFEE in inter-provincial or regional cities, TFEE is calculated with the ratio of target energy value and actual energy input based on data in cities of prefecture levels, which would be more accurate. Many researches regard TFP as TFEE to do analysis from the provincial perspective. This paper is intended to calculate more reliably by super efficiency DEA, observe the changes of TFEE, and analyze its relation with TFP, and it proves that TFP is not equal to TFEE. Additionally, the internal influences of the TFEE are obtained via the Malmquist index decomposition. The external influences of the TFFE are analyzed afterward based on the Tobit models. Analysis results demonstrate that Heilongjiang has the highest TFEE followed by Jilin, and Liaoning has the lowest TFEE. Eventually, some policy suggestions are proposed for the influences of energy efficiency and study results.