Does agricultural mechanization improve agricultural environment efficiency? Evidence from China’s planting industry

• Published in: Environmental science and pollution research international Vol. 29; no. 35; pp. 53673 - 53690
• Main Authors: Zhu, Yingyu, Zhang, Yan, Piao, Huilan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2022; Springer Nature B.V
• Subjects: Agricultural technology; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Autoregressive models; Carbon sinks; Earth and Environmental Science; Ecotoxicology; Empirical analysis; Energy consumption; Environment; Environmental Chemistry; Environmental Health; Environmental impact; Environmental science; Impact analysis; Independent variables; Mechanization; Optimization; Regression models; Research Article; Resource allocation; Robustness (mathematics); Stochasticity; Waste Water Technology; Water Management; Water Pollution Control; China; Net carbon sinks; Agricultural environment efficiency; Agricultural mechanization; Stochastic frontier analysis; Output-oriented distance function; Temporal cumulative effect; Spatial spillover effect
• ISSN: 0944-1344; 1614-7499
• DOI: 10.1007/s11356-022-19642-9

Environmental problems caused by energy consumption in the rapid popularization of China’s agricultural mechanization (AM) have caused increasing concern. Using the panel data of China’s 30 provinces from 2001 to 2019, this article adopts a stochastic frontier analysis method with output-oriented distance function to measure agricultural environment efficiency (AEE) based on net carbon sinks and empirically analyzes the impact of AM on AEE. The main findings are as follows: Firstly, the AEE of the nation and all provinces shows an upward trend over time and has significant spatial positive autocorrelation characteristics. Secondly, there is an inverted U-shaped relationship between AM and AEE. Meanwhile, AM has spatial spillover effect and time cumulative effect on AEE. These basic conclusions are still robust after using instrumental variables, spatial autoregressive model, sub-sample regression, changing spatial weight matrix, and independent variable. Thirdly, the effect of AM on AEE depends on the input effect and output effect caused by AM. The mechanism is mainly reflected in agricultural technology progress, expansion of the scale of farming operation, optimization of resource allocation, and spatial spillover. Given these findings, the paper adds considerable value to the empirical literature and provides various policy and practical implications.