Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper applies the zero-sum gains data envelopment analysis (ZSG-DEA) model to assess the provincial CO2 emission reduction scheme of China in 2020 and then calculates optimized values for the provinces in 2030. Results from two standard optimization procedures to solve the ZSG-DEA model are compared. We also extend the current methods by improving the iterative approach and then introduce fairness to the efficiency-oriented approach for the 2030 case. We found that the choice of optimization procedures has only a minimal impact on the results, but the specification of model does matter. For 2020, a standard DEA approach shows that only 2 provinces out of 30 are efficient. According to the estimation results of the ZSG-DEA model, 12 provinces need to set higher reduction targets, whereas the other 18 provinces could have lower target values to achieve overall efficiency. These results are generally consistent across different optimization approaches. The improved approach is applied to further evaluate a number of scenarios in 2030, in which the adjustment values towards optimum can be calculated for each scenario. Furthermore, we show that the inclusion of fairness can significantly affect the adjustment targets, which is of great importance to policymakers