Detection and attribution of changes in cultivated land use ecological efficiency: A case study on Yangtze River Economic Belt, China. (April 2022)

• Record Type: Journal Article
• Authors: Yin, Yuqing; Hou, Xianhui; Liu, Jingming; Zhou, Xin; Zhang, Daojun
• Is Part Of: Ecological indicators. Volume 137(2022)
• Publication Date: 2022-04
• Subjects: Cultivated land use eco-efficiency -- Heterogeneous agricultural production environment -- Geographically weighted regression model -- Sustainable land use -- Yangtze River Economic Belt; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2022.108753
• Languages: English
• ISSNs: 1470-160X

Abstract

Graphical abstract: Highlights: The three-stage super-efficiency SBM-U model was used to evaluate CLUE of YREB in China. Heterogeneous agricultural production environment play a vital role in CLUE evaluation. The OLS and GWR models were used to analyze the driving mechanism of CLUE. The driving mechanism of CLUE is spatiotemporal non-stationary. Input-output structure and production factor substitution are main determinants. Abstract: Improving the cultivated land use ecological efficiency (CLUE) to obtain greater agricultural output at a lower environmental cost, is an important part of effective management of land resources and sustainable intensification (SI). However, when evaluating CLUE, the existing research ignores the heterogeneity of the production environment of different agricultural production units, which leads to a "distortion effect". To address this, this study focuses on the Yangtze River Economic Belt (YREB) in China as the study area and uses a three-stage super-efficiency slacks-based measure-undesirable (three-stage super-efficiency SBM-U) model to evaluate CLUE after excluding the interference of a heterogeneous agricultural production environment. In addition, we constructed ordinary least squares (OLS) and geographically weighted regression (GWR) models to analyze the driving mechanism of CLUE. The results indicate that environmental factors have a significant impact on the evaluation results of CLUE. Ignoring environmental factors leads to an overestimation of CLUE in areas with better environmental conditions, and vice versa. Second, due to the difference in input–output structure and the varied difficulty in production factor substitution, the driving mechanism of CLUE is spatiotemporal non-stationarity. During the study period, gross domestic product per capita, and farmland management scale had a positive effect on CLUE, while industrialization level had a negative effect. The impact of fiscal support for agriculture changed from insignificant to significant, in terms of improving CLUE in the western YREB. The influence direction of the multiple cropping index and agricultural planting structure changes from positive to negative and is concentrated in the east of the YREB. As a result, a series of measures are proposed to promote the improvement of CLUE and realize SI.