Data-driven allocation of renewables quota among regional power industries under the policy of renewable electricity standard

China is struggling to facilitate the application of renewable portfolio standards to realize sustainable economic growth. As such, improving the current distribution mechanism is crucial. In this paper, the context-dependent data envelopment analysis and multi-objective linear programming are combined to allocate the renewables quota for each province. This integrated approach can maximize total electricity generation while minimizing the total CO2 emission with considering the disparity of production technology level. Then, the extended Gini coefficient is employed to assess the fairness of new quota mechanism. We find that (1) the eastern region is the most efficient during the power generation process. During 2016-2019, the efficiency in the western region presents an upward trend. (2) The allocation results indicate that Inner Mongolia and Qinghai have the greatest pressure to absorb renewable energy electricity, while Guangdong and Guizhou can instead reduce the most. Shandong and Inner Mongolia face the greatest burden in conserving non-renewable electricity. (3) Compared to 2020, the newly allocated scheme can mitigate inequality, with the Gini coefficient changing from 0.264 in 2020 to 0.248 after the allocation. Meanwhile, the reallocation reduces the Gini coefficient related to renewable electricity, non-renewable electricity, and CO2 emissions by 0.003, 0.028, and 0.073, respectively at the 2020 level.