The improvement gap in energy intensity: Analysis of China's thirty provincial regions using the improved DEA (data envelopment analysis) model

• We present an improved DEA model to calculate the TFEE (total-factor energy efficiency).
• The improved TFEE combines with a meta-frontier analysis.
• We estabilish a new indicator for improvement gap in energy intensity.
• Improvement in energy intensity of regions in China is analysed.

Enacting a reduction target for energy intensity in provinces has become an important issue for the central and local governments in China. But the energy intensity index has provided little information about energy efficiency improvement potential. This study re-estimates the TFEE (total-factor energy efficiency) using an improved DEA (data envelopment analysis) model, which combines the super-efficiency and sequential DEA models to avoid “discriminating power problem” and “technical regress”, and then used it to calculated the TEI (target for energy intensity). The REI (improvement potential in energy intensity) is calculated by the difference between TEI and the actual level of energy intensity. In application, we calculate the REIs for different provinces under the metafrontier and group-frontier respectively, and their ratios are the technology gaps for energy use. The main result shows that China's REIs fluctuate around 21%, 7.5% and 12% for Eastern, Central and Western China respectively; and Eastern China has the highest level of energy technology. These findings reveal that energy intensities of China's provinces do not converge to the optimal level. Therefore, the target of energy-saving policy for regions should be enhancing the energy efficiency of the inefficient ones, and thereby reduce the gap for improvement in energy intensity across regions.