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A study on the effects of regional differences on agricultural water resource utilization efficiency using super-efficiency SBM model
This study evaluated the water resource utilization efficiency and resource consumption for planting, forestry, animal husbandry, and fishery in various regions of China. Using the super-efficiency Slacks-Based Measure (SBM) analysis method, the main agricultural pollution emissions (Chemical Oxygen...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8113275/ https://www.ncbi.nlm.nih.gov/pubmed/33976285 http://dx.doi.org/10.1038/s41598-021-89293-2 |
Sumario: | This study evaluated the water resource utilization efficiency and resource consumption for planting, forestry, animal husbandry, and fishery in various regions of China. Using the super-efficiency Slacks-Based Measure (SBM) analysis method, the main agricultural pollution emissions (Chemical Oxygen Demand, ammonia nitrogen, and agricultural carbon emissions) were proposed as environmental constraints for the first time. The threshold regression model was used to measure the impact of agricultural water use efficiency on agricultural water consumption by constructing seven different explanatory variables. The results show that the overall utilization efficiency of agricultural water resources in China presents a fluctuating downward trend, and the regional differences are significant. A single threshold effect on agricultural water consumption was found in five variables: per capita water resources, disposable income, dependence on foreign trade, industrial structure, and Gross Domestic Product. The increase in each parameter will have a positive effect on agricultural water consumption. The relationship between agricultural water use efficiency and agricultural water use was non-linear when the government's attention and the rural labor force were used as threshold variables. |
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