Enhancing energy ecological efficiency is a realistic choice for realizing energy saving and emission reduction and constructing ecological civilization during the 13 th Five-Year Plan period in china. Considering ecological and social welfare factors, we used a slacks-based measure data envelopment analysis(SBM-DEA) model to calculate and analyze regional energy ecological efficiency in China from 2000 to 2016 by applying provincial panel data, and utilized a fixed effect model to explore its impact mechanism from the perspective of fiscal decentralization and economic competition. The results indicate that the energy ecological efficiency level in the central and western regions of China was worse than that in the eastern region, and the central region presented a continuous deterioration trend for the 17 years duration. Financial decentralization significantly improved energy ecological efficiency. However, economic competition among local governments reduced energy ecological efficiency. The intersection of fiscal decentralization and economic competition described that economic competition under Chinese fiscal decentralization has worsened China’s energy ecological efficiency. The regional dummy variables showed that economic competition significantly damaged energy ecological efficiency in the eastern and central regions while it helped to improve energy ecological efficiency in western regions. Based on a counterfactual analysis, if the Chinese government would curb the severe intensification of economic competition, energy ecological efficiency can get an average of 1.38% extra improvement each year. At last, we propose policy proposals such as reforming the fiscal decentralization incentive system, strengthening the accountability of audit supervision, upgrading the industrial structure according to its own characteristics, and encouraging local governments to form economic competition and cooperation may be beneficial to raise China’s energy ecological efficiency.
energy ecological efficiency
slacksbased measure data envelopment analysis