Analysis on the spatiotemporal patterns and driving mechanisms of China's agricultural production efficiency from 2000 to 2015

Guo, Baishu; He, Dawei; Zhao, Xiaodong; Zhang, Zhengyu; Yin, Daqiang

doi:10.1016/j.pce.2020.102909

Cited by 66 publications

(48 citation statements)

References 46 publications

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“…Guo et al used labor, land, and capital as input variables and agricultural economic benefits as output variables. There, they used the Stochastic Frontier Approach (SFA) model, spatial correlation analysis method, and Tobit model to effectively evaluate the spatial layout of agricultural production and APE optimization [ 8 ]. Li et al used the data envelopment analysis (DEA) model to estimate the technical efficiency and energy-saving potential of China’s 30 provincial agricultural departments from 1997–2014 and concluded that there are differences in APE in the eastern and western regions of China [ 9 ].…”

Section: Literature Reviewmentioning

confidence: 99%

An Analysis of Agricultural Production Efficiency of Yangtze River Economic Belt Based on a Three-Stage DEA Malmquist Model

Pan

Tang

Kong

et al. 2022

IJERPH

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The Yangtze River Economic Belt (YREB) is a major national strategic development area in China, and the development of the YREB will greatly promote the development of the entirety China, so research on its agricultural production efficiency is also of great significance. This paper is committed to studying the agricultural production efficiency of 11 provinces in the YREB and adopts a combination of the Data Envelopment Analysis (DEA) model and the Malmquist index to make a dynamic and static analysis on the YREB’s agricultural production efficiency from 2010 to 2019. Then, a three-stage DEA Malmquist model that eliminates the factors of random interference and management inefficiency is compared to a model without elimination. The results show that the adjusted technological efficiency changes, technological progress, and total factor productivity increased by −0.1%, 0.24%, and 0.22%, respectively. When comparing these values to the pre-adjustment values, the results indicate that the effect of environmental variables cannot be ignored when studying the agricultural production efficiency of the YREB. At the same time, the differences in the agricultural production efficiency in the YREB are reasonably explained, and feasible suggestions are put forward.

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Section: Literature Reviewmentioning

confidence: 99%

An Analysis of Agricultural Production Efficiency of Yangtze River Economic Belt Based on a Three-Stage DEA Malmquist Model

Pan

Tang

Kong

et al. 2022

IJERPH

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“…The phenomenon of machines replacing humans in agricultural production has become more and more common, which has promoted the improvement of agricultural green production efficiency [54]. Meanwhile, rapid economic development is often accompanied by the occupation of agricultural land and other resources, so that the supply of farmland and other agricultural production factor resources cannot be guaranteed [55]. (2) The development of urbanization hinders the improvement of agricultural green production efficiency.…”

Section: Analysis Of External Driving Factors Of Agricultural Green Production Efficiencymentioning

confidence: 99%

Analysis on the Agricultural Green Production Efficiency and Driving Factors of Urban Agglomerations in the Middle Reaches of the Yangtze River

Wang

Pang

et al. 2020

Sustainability

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As one of the main grain-producing areas in China, urban agglomeration in the middle reaches of the Yangtze River plays an important role in the development of agricultural production for China’s grain supply. The existing studies about agricultural production efficiency lack of regional coordination analysis at both macro and micro levels, and only few studies consider the impact of agricultural production environment pollution and other undesirable outputs. Based on the input–output index system of agricultural green production, Slacks-based model (SBM) was adopted to measure the agricultural green production efficiency of 31 prefecture level cities in the middle reaches of the Yangtze River from 2008 to 2018, and the Tobit model of panel fixed effect was used to analyze the driving effect of external factors that affect the agricultural green production efficiency of urban agglomeration in the middle reaches of the Yangtze River. At the same time, the research methods at both macro and micro levels provide ideas for the research of transregional production efficiency. The results showed that: (1) the agricultural green production efficiency of urban agglomeration in the middle reaches of the Yangtze river is relatively low, with 2009 and 2013 as the inflection points, showing a stable trend of rise and decline; (2) The green agricultural production efficiency of urban agglomeration in the middle reaches of the Yangtze River presents the spatial distribution characteristics of “high in the west and low in the east”. The regional efficiency difference is obvious, the gap gradually expands, develops from the equilibrium to the polarization; (3) Urbanization development and government intervention has a significant restraining effect on the improvement of agricultural green production efficiency, and opening to the outside world produces a remarkable influence on the improvement of agricultural green production efficiency, however, economic development and industrial structure have little impact on the improvement of agricultural green production efficiency. Therefore, it is necessary to increase investment in technological innovation, promote agricultural transformation and upgrading, promote rational factors allocation and promote coordinated development of agriculture based on regional production differences.

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“…On the one hand, in remote rural areas, the natural, cultural and ethnic barriers make it difficult for the poor population to flow between regions (Jalan & Ravallion, 1999;Jin et al, 2018), and the social network of the family is usually limited in a small space. In addition, there is a more significant intergenerational transmission of poverty in the farm households of poor areas (Liu et al, 2017;Guo et al, 2020). Therefore, geographical isolation severely reduces the effects of social capital on rural poverty reduction.…”

Section: Literature Reviewmentioning

confidence: 99%

Expenditure Fluctuation and Consumption Loss: Rural Spatial Poverty in China

Luo

Qin

Qing

2021

Technological and Economic Development of Economy

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Poverty is a challenge faced by all countries worldwide. This paper focuses on a factor that has been less well documented: the consumption loss of farmer households caused by the fluctuation of rural public expenditure. Based on large-scale micro household data and climate data, the instrumental variable estimation results show that every 1% fluctuation of rural public expenditure will lead to a 0.113% decrease in farm household consumption. In addition, the fluctuation of rural public expenditure is also a main cause of long-term consumption loss of farmer households. Furthermore, it was found that the impact of rural public expenditure fluctuation on consumption loss is of certain spatial heterogeneity. The worse the spatial environment is, the more serious the consumption loss will be. The policy suggestion of this paper is to ensure a stable scale of rural public expenditure through the central transfer payment, so as to improve the ability of local governments to implement counter cyclical public policies, and transform local finance (industrial investment) into public finance (infrastructure and education) to improve the local space environment. Overall, this study reveals the impact of spatial externality on rural poverty from the perspective of public expenditure fluctuation, and at the same time provides empirical evidence for a better evaluation of the relationship between development and poverty and support for rational regional anti-poverty policies.

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Analysis on the spatiotemporal patterns and driving mechanisms of China's agricultural production efficiency from 2000 to 2015

Cited by 66 publications

References 46 publications

An Analysis of Agricultural Production Efficiency of Yangtze River Economic Belt Based on a Three-Stage DEA Malmquist Model

An Analysis of Agricultural Production Efficiency of Yangtze River Economic Belt Based on a Three-Stage DEA Malmquist Model

Analysis on the Agricultural Green Production Efficiency and Driving Factors of Urban Agglomerations in the Middle Reaches of the Yangtze River

Expenditure Fluctuation and Consumption Loss: Rural Spatial Poverty in China

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