Analysis of the Spatial Differentiation and Promotion Potential for Agricultural Eco-Efficiency—Evidence of Pollution’s Strong Disposability

Abstract

Agricultural eco-efficiency is an important indicator used to measure agriculture’s high-quality and sustainable development. Therefore, this paper uses the EBM-Super-ML method with strong disposability of undesired output to calculate Chinese agricultural eco-efficiency and uses a geographical detector to measure the driving force of the factor. The research conclusions are mainly reflected in three aspects. Firstly, from the perspective of eco-efficiency changes, the overall mean value of agricultural eco-efficiency increased by 3.5%, and the regional heterogeneity is significant, with the fastest growth in the eastern region. Secondly, the results of driving force analysis show that the main driving factors of agricultural eco-efficiency divergence are capital inputs, total carbon emissions, labor inputs, agricultural film residues, fertilizer use, and pesticide residues, with driving forces of 0.43, 0.37, 0.34, 0.31, 0.28, and 0.20, respectively. Finally, from the perspective of eco-efficiency improvement potential, the mean value of output improvement potential is 5%, and the input factor is 7%. Among the non-desired outputs, the reduction rate of agricultural films can reach 40%. Among the input factors, labor input has the highest potential for intensive use, while agricultural machinery has a negative effect. Therefore, strengthening the development of the agricultural service industry is of great significance to improve the utilization rate of mechanical equipment and reduce the undesired output of agriculture.

Temporal-Spatial Dynamic Characteristics and the Determinants of Agricultural Eco-efficiency in Fujian Province, China

“…Among them, the superefficient SBM model with non-expected output has unique advantages in measuring AEE, which can effectively avoid the bias caused by radial and angular metrics, and consider the influence of non-expected output factors, which better reflects the nature of the efficiency evaluation, and has been widely cited by the academic community. (3) In terms of the factors affecting AEE, scholars have used the Tobit model [19], the STIRPAT model [20], and geographical detector [21] to analyze the impact of elements such as the urbanization rate [19], agricultural science and technology inputs [22], resource endowments [23] and low-carbon pilot policy [24] on the AEE. The green and low-carbon development of agriculture is a systematic project, and the coordinated development among the constituent elements is the key.…”