Measuring regional efficiency of energy and carbon dioxide emissions in China: A chance constrained DEA approach

There exist multiple randomness errors (commonly regarded as the uncertainty) in the estimation of CO2 emissions. This uncertainty has been an important issue in regional energy use and carbon dioxide (CO2) emissions efficiency evaluation. To address this issue, a radial stochastic DEA model is proposed based on chance constrained programming. Then, the radial stochastic model is extended to a non-radial model for measuring pure energy use and CO2 emissions efficiencies. Based on the stochastic non-radial model, the measures of energy efficiency, CO2 emissions efficiency, energy saving potential and CO2 emissions reduction potential are provided. The proposed approach has been applied to evaluate regional efficiencies of energy use and CO2 emissions in China by using the data set in 2010. The empirical study results show that the uncertainty of CO2 emissions has significant influences on regional efficiencies of energy use and CO2 emissions, especially the efficiency of CO2 emissions, and the proposed stochastic models can effectively deal with the uncertainty of CO2 emissions in the process of efficiency evaluation. ●A stochastic DEA approach considering CO2 emissions uncertainty is proposed.●The uncertainty of CO2 emissions has significant impacts on regional efficiencies.●Regional inefficiency in China is mainly caused by lower CO2 emissions performance.●Efforts should be taken to reducing the uncertainty of CO2 emissions in China.