Evaluation and improvement of agricultural green total factor energy efficiency: The perspective of the closest target

Abstract

The effective evaluation and improvement of agricultural green total factor energy efficiency (AGTFEE) are crucial for guiding sustainable agricultural development. The directional distance function (DDF), which can evaluate efficiency values and provide efficiency improvement paths, has attracted widespread attention. However, most existing research on DDF is based on the farthest target principle, often resulting in costly efficiency improvement paths. To address this issue, this study proposes a novel cross-DDF based on a learning network under the closest target principle. The proposed model is applied to dynamically analyze AGTFEE in China from 2013 to 2022 at different levels. Compared with existing research, the proposed model offers more feasible and cost-effective quantitative paths for improving AGTFEE. Moreover, the proposed model constructs a learning network based on the interactions among decision-making units for peer evaluation, avoiding inflated efficiency values. The empirical results highlight three main findings. First, over the decade from 2013 to 2022, China's AGTFEE has exhibited a positive trend, achieving significant progress. Second, during the same period, the balance and consistency of AGTFEE development have improved. However, differences remain among regions and provinces, with the distribution pattern showing “best in the east, followed by the west, and relatively poor in the center.” Third, there are differences in the improvement paths for AGTFEE among provinces. For instance, to improve AGTFEE in Hebei Province in 2022, it is necessary to significantly reduce the amount of pesticides used in the agricultural production process.