Agricultural infrastructure: The forgotten key driving force of crop-related water footprints and virtual water flows in China

Huang, Hongrong; Zhuo, La; Wang, Ranran; Shang, Kehui; Ли, Менг; Yang, Xi; Wu, Pute

doi:10.1016/j.jclepro.2021.127455

Cited by 24 publications

(13 citation statements)

References 61 publications

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“…Agricultural science and technology input was measured by the stock of agricultural science and technology capital, which needed to be estimated based on the “perpetual inventory method” [ 43 ]. The length of highways divided by the administrative area of the province equals agricultural infrastructure [ 44 ]. Rural human capital was based on Hall and Jones by using the conversion method of education years, and the degree of industrialization was measured by the total value of non-agricultural output divided by Gross Domestic Product (GDP) [ 45 ].…”

Section: Methodsmentioning

confidence: 99%

Spatial Distribution and Convergence of Agricultural Green Total Factor Productivity in China

Zhu

Shi

et al. 2022

IJERPH

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The power source, spatial-temporal differentiation and convergence of the growth rate of green total factor productivity in China’s agriculture were analyzed. The Malmquist index was used to measure the growth rate, and the spatial-temporal convergence was tested by σ convergence, absolute β convergence, conditional β convergence and dynamic spatial convergence. The study drew conclusions that the impetus for the intensive growth of green agriculture was insufficient, and the driving force for the growth of agricultural green total factor productivity (AGTFP) in the eastern, western and central region was green technology progress. In addition, AGTFP did not have an absolute σ convergence trend. Dynamic spatial absolute β and conditional β convergence indicated that regional differences were not completely related to regional endowment conditions, and regional green agricultural production was unbalanced. This study provides an important support for regional green development in China’s agriculture.

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Section: Methodsmentioning

confidence: 99%

Spatial Distribution and Convergence of Agricultural Green Total Factor Productivity in China

Zhu

Shi

et al. 2022

IJERPH

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“…The SDA method is based on an input-output model and requires input-output coefficients and final demand for each sector (Su and Ang 2012;Jin et al, 2016); however, the LMDI method has no residual term (Ang 2005). The LMDI cannot examine the elasticity of each factor, which refers to the change in the dependent variable due to a change in an independent variable, while other factors remain constant (Huang et al, 2021). Compared with other methods, the STIRPAT model is more flexible and easier to operate, clarifying the mathematical relationship between driving forces and their effects Jin et al, 2016).…”

Section: Open Accessmentioning

confidence: 99%

Driving factor analysis of spatial and temporal variations in the gray water footprint of crop production via multiple methods: A case for west China

Gao

Zhao²,

Zhang³

et al. 2023

Front. Environ. Sci.

Self Cite

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The gray water footprint (GWF) can quantitatively evaluate the effect of non-point pollution on water quality in the context of water quantity. It is crucial to explore the driving forces behind the GWF to solve water quality problems. This study quantified the unit GWFs of grain crops and oil crops at the municipal scale in six provinces of western China over 2001–2018, then jointly applied the extended STIRPAT model and path analysis methods to analyze the climatic and socioeconomic driving forces of the GWF. Results show that the key driving forces affecting the GWF obtained by the two methods were consistent. Planting structure and population were the main factors increasing the total GWF, while crop yield was the largest factor inhibiting the unit GWF and demonstrates regional differences. However, when the indirect influence of the driving factor through other factors was large, some driving forces obtained by different methods were reversed. For example, the indirect impact of per capita cultivated land area on the total GWF in Inner Mongolia was large, resulting in a significant positive impact in path analysis and a slight negative impact in the STIRPAT model. To draw more comprehensive and referential conclusions, we suggest using multiple methods together to verify the driving forces and account for the regional differences.

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“…Between 1980 and 2020, while the per capita cultivated land area showed a trend of fluctuating decrease, the area of embankment protected cultivated land still showed a trend of fluctuating increase. After the 1990s in particular, China implemented the eighth five-year Plan (1991-1995), which made clear arrangements for the development of agriculture and water conservancy, and greatly promoted the infrastructure construction [68]. During this period, embankment-protected cultivated land was increased greatly, which improved the disaster resistance level of agriculture and the resilience level of rural residents' livelihoods.…”

Section: Influencing Factors Of Rural Residents' Livelihood Resiliencementioning

confidence: 99%

Livelihood Resilience of Rural Residents under Natural Disasters in China

Liu

Pan

et al. 2022

Sustainability

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The impact of natural disasters on rural areas has recently increased, the question of how to effectively improve the livelihood resilience of rural residents has therefore become an essential issue. In this context, a livelihood resilience evaluation index system for rural residents was constructed in four dimensions: livelihood quality, livelihood promotion, livelihood provision, and disaster stress. A structural dynamics model was used to analyze the changing characteristics of the livelihood resilience of rural residents in China between the years 1980 and 2020, and a livelihood resilience trend from 2021 to 2030 was predicted based on the ARIMA model. The main factors influencing livelihood resilience were explored using ridge regression analysis. The results show that: (1) livelihood resilience of rural residents in China fluctuated significantly between 1980 and 2020, tending generally to increase; (2) livelihood resilience is positively correlated with livelihood quality, livelihood promotion, and livelihood provision, while it is negatively correlated with disaster stress; (3) livelihood quality, livelihood promotion, and livelihood supply will still increase between the years 2021 and 2030, while the increase of livelihood resilience will tend to slow down; and (4) six variables have a significant positive impact on livelihood resilience, and provide a basis for the subsequent enhancement of livelihood resilience.

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Agricultural infrastructure: The forgotten key driving force of crop-related water footprints and virtual water flows in China

Cited by 24 publications

References 61 publications

Spatial Distribution and Convergence of Agricultural Green Total Factor Productivity in China

Spatial Distribution and Convergence of Agricultural Green Total Factor Productivity in China

Driving factor analysis of spatial and temporal variations in the gray water footprint of crop production via multiple methods: A case for west China

Livelihood Resilience of Rural Residents under Natural Disasters in China

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