摘要
基于耕地生产力视角,运用扩展三维生态足迹模型刻画兰州市2010~2019年耕地资本存量消耗、流量占用及生态承载力的动态变化情况。然后,从耕地生产性利用和供给两个角度深入分析兰州市耕地现状并利用偏最小二乘回归法定量识别影响耕地生产性生态足迹和生态承载力的社会经济因素。结果表明:(1)近年来兰州市耕地占用基本处于可持续状态,耕地流量可以满足人类消费需求。(2)人口增长在增加耕地生产性生态足迹的同时会减少生态承载力,经济发展会同时增加耕地生产性生态足迹和生态承载力,技术提高不仅可以减少耕地生产性生态足迹,还能有效增加生态承载力。
Based on the perspective of cultivated land productivity,the extended three-dimensional ecological footprint model is used to describe the dynamic changes of cultivated land capital stock consumption,flow occupation and ecological carrying capacity in Lanzhou from 2010 to 2019.Then the current situation of cultivated land in Lanzhou are deeply analyzed from the perspectives of productive utilization and supply of cultivated land,and the socio-economic factors affecting the productive ecological footprint and ecological carrying capacity of cultivated land are quantitatively identified by partial least square regression method.The results show that:①the occupation of cultivated land in Lanzhou is basically sustainable in recent years,and the cultivated land flow can meet the demand of human consumption.②Population growth will increase the productive ecological footprint of cultivated land and reduce the ecological carrying capacity of cultivated land,economic development will increase the productive ecological footprint and ecological carrying capacity of cultivated land at the same time,technology improvement can not only reduce the productive ecological footprint of cultivated land,but effectively improve the ecological carrying capacity of cultivated land.
作者
闫瑞雯
王永瑜
Yan Ruiwen;Wang Yongyu(School of Statistics,Beijing Normal University Beijing 100875;School of Statistics,Lanzhou University of Finance and Economics Lanzhou 730020)
出处
《经济统计学(季刊)》
2024年第1期43-58,共16页
China Economic Statistics Quarterly
基金
国家社会科学基金一般项目“中国自然资源产品供给与使用表编制方法及应用研究”(项目编号:22BTJ002)
关键词
耕地生态承载力
生产性生态足迹
扩展三维生态足迹模型
STIRPAT理论
偏最小二乘回归
Ecological Carrying Capacity of Cultivated Land
Productive Ecological Footprint
Extended Three-dimensional Ecological Footprint Model
STIRPAT Theory
Partial Least Squares Regression