Spatiotemporal characteristics of cultivated land use eco-efficiency and its influencing factors in China from 2000 to 2020

被引:0
|
作者
Shaoting Li
Na Mu
Yanjun Ren
Thomas Glauben
机构
[1] Northwest A&F University,College of Economics and Management
[2] Northwest A&F University,Sino
[3] Leibniz Institute of Agricultural Development in Transition Economies (IAMO),German Center for Agricultural and Food Economics
来源
Journal of Arid Land | 2024年 / 16卷
关键词
cultivated land use eco-efficiency (CLUE); slacks-based measure (SBM) model; barycentre model; standard deviation ellipse (SDE); panel Tobit model; carbon emissions; nonpoint source pollution;
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中图分类号
学科分类号
摘要
Improving cultivated land use eco-efficiency (CLUE) can effectively promote agricultural sustainability, particularly in developing countries where CLUE is generally low. This study used provincial-level data from China to evaluate the spatiotemporal evolution of CLUE from 2000 to 2020 and identified the influencing factors of CLUE by using a panel Tobit model. In addition, given the undesirable outputs of agricultural production, we incorporated carbon emissions and nonpoint source pollution into the global benchmark-undesirable output-super efficiency-slacks-based measure (GB-US-SBM) model, which combines global benchmark technology, undesirable output, super efficiency, and slacks-based measure. The results indicated that there was an upward trend in CLUE in China from 2000 to 2020, with an increase rate of 2.62%. The temporal evolution of CLUE in China could be classified into three distinct stages: a period of fluctuating decrease (2000–2007), a phase of gradual increase (2008–2014), and a period of rapid growth (2015–2020). The major grain-producing areas (MPAs) had a lower CLUE than their counterparts, namely, non-major grain-production areas (non-MPAs). The spatial agglomeration effect followed a northeast-southwest strip distribution; and the movement path of barycentre revealed a “P” shape, with Luoyang City, Henan Province, as the centre. In terms of influencing factors of CLUE, investment in science and technology played the most vital role in improving CLUE, while irrigation index had the most negative effect. It should be noted that these two influencing factors had different impacts on MPAs and non-MPAs. Therefore, relevant departments should formulate policies to enhance the level of science and technology, improve irrigation condition, and promote sustainable utilization of cultivated land.
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页码:396 / 414
页数:18
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