Estimation of industrial energy efficiency and corresponding spatial clustering in urban China by a meta-frontier model

Energy efficiency is an important measure of the energy utilization performance, which should be evaluated in industrial production. This paper proposes an improved slacks-based data envelopment analysis (DEA) and spatial clustering analysis to investigate urban efficiency considering regional technological heterogeneity and the presence of carbon emissions. The technological and managerial inefficiencies of industrial energy utilization are first explored by considering neighboring performance. The proposed method is applied to an empirical study including data on 277 cities in China during 2007-2014. Some conclusions can be drawn: (1) there are significant spatial disparities in the energy efficiency, technology gap rate and managerial inefficiency in cities. Improvements in technology and management should be decided on depending upon the urban conditions. (2) There is evident spatial clustering in energy efficiency, technology gap rate and managerial inefficiency for partial cities. Importantly, management improvement has a more positive effect on the energy saving than technological improvement, except for cities in the low-low clustering pattern. (3) The negative technology gap rate reflects resource misallocation for technological improvement. For some underdeveloped cities, investment in technology should be controlled.