Fu Jun, Ding Rui, Zhu Yu-Qi, Du Lin-Yu, Shen Si-Wei, Peng Li-Na, Zou Jian, Hong Yu-Xuan, Liang Juan, Wang Ke-Xin, Xiao Wen-Qian
College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China; Guizhou Collaborative Innovation Center of Green Finance and Ecological Environment Protection, Guiyang 550025, China; Artificial Intelligence and Digital Finance Lab, Guizhou University of Finance and Economics, Guiyang 550025, China.
College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China; Guizhou Collaborative Innovation Center of Green Finance and Ecological Environment Protection, Guiyang 550025, China; Artificial Intelligence and Digital Finance Lab, Guizhou University of Finance and Economics, Guiyang 550025, China.
Environ Res. 2023 Nov 15;237(Pt 1):116881. doi: 10.1016/j.envres.2023.116881. Epub 2023 Aug 16.
Agricultural land is the most basic input factor for agricultural production and an essential component of terrestrial ecosystems, which plays a vital role in achieving carbon neutrality. Giving full play to the carbon-neutral contribution of agricultural land is a crucial part of China's economic transformation and green development. It incorporates carbon and pollution emissions from agricultural land use into the unexpected outputs of the Green and Low-carbon Utilization Efficiency of Agricultural Land (GLUEAL) evaluation system. The study utilized several advanced analytical tools, including the super-efficient Slacks-Based Measure (SBM) model, Exploratory Spatial-Temporal Data Analysis (ESTDA) method, Geodetector, and Geographically and Temporally Weighted Regression (GTWR) model. The objective was to examine the spatial-temporal evolution of GLUEAL and identify the factors that influenced it in all 31 provinces of China from 2005 to 2020. The results show that: (1) The overall spatial-temporal evolution of GLUEAL showed an increasing trend, but the disparity between provinces and regions became wider. (2) Most provinces have not yet made significant spatial and temporal jumps. They have high spatial cohesion with specific "path-dependent" characteristics. (3) The Geodetector results reveal that the Number of Rural Labor Force with Higher Education (NRLFHE) and Technology Support for Agriculture (TSA) have insufficient explanatory power on average for GLUEAL. Agricultural Economic Development Level (AEDL), Urbanization Level (UL), Multiple Crop Index (MCI), Planting Structure (PS), Degree of Crop Damage (DCD), Financial support for agriculture (FSA), and Agricultural mechanization level (AML) had stronger explanatory power on average for GLUEAL and were important factors influencing GLUEAL levels. (4) The average influence of AEDL, UL, FSA, and AML on GLUEAL changed from negative to positive. The average influence of MCI and DCD on GLUEAL was negative, and the average influence of PS on GLUEAL changed from positive to negative. This study provides a comprehensive description of the spatial and temporal evolution of GLUEAL in China. It reveals the key factors influencing GLUEAL and analyzes their spatial variations and impact patterns. These findings offer robust evidence for government policymakers to formulate policy measures for sustainable agricultural development and optimized resource allocation, promoting the transformation of agricultural land towards green and low-carbon practices and advancing the achievement of sustainable development goals.
农业用地是农业生产最基本的投入要素，也是陆地生态系统的重要组成部分，在实现碳中和方面发挥着至关重要的作用。充分发挥农业用地的碳中和贡献是中国经济转型和绿色发展的关键环节。它将农业用地利用过程中的碳排放和污染排放纳入农业用地绿色低碳利用效率（GLUEAL）评价体系的非期望产出中。该研究运用了多种先进分析工具，包括超效率松弛测度（SBM）模型、探索性时空数据分析（ESTDA）方法、地理探测器以及地理时空加权回归（GTWR）模型。目的是考察2005年至2020年中国31个省份GLUEAL的时空演变，并识别影响其变化的因素。结果表明：（1）GLUEAL的总体时空演变呈上升趋势，但省际和区域间差距扩大。（2）多数省份尚未出现显著的时空跳跃，具有较高的空间凝聚性和特定的“路径依赖”特征。（3）地理探测器结果显示，农村高等教育劳动力数量（NRLFHE）和农业技术支持（TSA）对GLUEAL的平均解释力不足。农业经济发展水平（AEDL）、城市化水平（UL）、复种指数（MCI）、种植结构（PS）、作物受灾程度（DCD）、农业财政支持（FSA）和农业机械化水平（AML）对GLUEAL的平均解释力较强，是影响GLUEAL水平的重要因素。（4）AEDL、UL、FSA和AML对GLUEAL的平均影响由负转正。MCI和DCD对GLUEAL的平均影响为负，PS对GLUEAL的平均影响由正转负。本研究全面描述了中国GLUEAL的时空演变，揭示了影响GLUEAL的关键因素，分析了其空间差异和影响模式。这些研究结果为政府政策制定者制定可持续农业发展和优化资源配置的政策措施提供了有力依据，推动农业用地向绿色低碳方式转变，促进可持续发展目标的实现。
