Spatial-temporal characteristics and influencing factors of agricultural eco-efficiency in China in recent 40 years

Liu, Yansui; Zou, Li; Wang, Yongsheng

doi:10.1016/j.landusepol.2020.104794

Cited by 176 publications

(149 citation statements)

References 54 publications

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“…According to Zhang et al [ 29 ], agricultural production efficiency is not only affected by the input and output factors but also influenced by the external environmental factor. In addition to the effect of farmland input factors, they also include local socioeconomic development [ 30 ], policy environment [ 5 ], and information channel [ 31 ]. These factors will directly affect the input elements and, thus, affect their agricultural production efficiency.…”

Section: Research Methods and Variables’ Definitionmentioning

confidence: 99%

Smallholders’ Agricultural Production Efficiency of Conservation Tillage in Jianghan Plain, China—Based on a Three-Stage DEA Model

Yang

Shang

2020

IJERPH

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Based on interviews with 695 smallholders in Jianghan Plain, this paper introduced the three-stage data envelopment analysis (DEA) model to analyze the agricultural production efficiency of conservation tillage adopters and explored the impact of environmental factors on agricultural production efficiency. The empirical results showed the following (1) Planting area, seed consumption, labor input, pesticide usage, chemical fertilizer usage, agricultural film usage were selected as input indicators, agricultural output was chosen as an output indicator, and the traditional DEA model was used to calculate the production efficiency of smallholders, and the agricultural production efficiency of smallholders was found to be at a low level. In addition, environmental and random factors both have significant impacts on efficiency, so they should be stripped. (2) After excluding environmental factors and random factors, the drop in pure technical efficiency of smallholders in the third stage was higher than the drop in scale efficiency when compared with the first stage. Moreover, the true technical efficiency was the main restricting factor for the agricultural production efficiency. (3) Educational level of smallholders, policy support, and information acquisition were the factors that affect the technical efficiency significantly. Improving the efficiency of agricultural production technology for smallholders requires strengthening rural basic education, improving subsidy policies for conservation agricultural technology, and establishing and improving rural information technology services.

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Section: Research Methods and Variables’ Definitionmentioning

confidence: 99%

Smallholders’ Agricultural Production Efficiency of Conservation Tillage in Jianghan Plain, China—Based on a Three-Stage DEA Model

Yang

Shang

2020

IJERPH

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“…Deng and Gibson (2019) estimated the agricultural eco-efficiency of Shandong in 1990-2010 based on stochastic frontier analysis. Liu et al (2020b) estimated the agricultural ecoefficiency of Chinese provinces over the period 1978-2019. By applying the nonseparable hybrid DEA model considering undesirable outputs, Han and Zhang (2020) evaluated environmental efficiency and the total factor productivity of cultivated land use.…”

Section: Agricultural Eco-efficiencymentioning

confidence: 99%

Evaluation and Influencing Factors of Agricultural Eco-efficiency in Jilin Agricultural Production Zone Based on Super-SBM and Panel Regression Methods

Yan-hua¹,

Tong²,

Lin³

2021

Preprint

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Agricultural eco-efficiency is a meaningful index that assess the agricultural sustainable development. Based on the super SBM-DEA approach incorporating agricultural carbon emissions and panel data regression, this study evaluates agricultural eco-efficiency and then investigates the influencing factors in agricultural production zone of Jilin Province. The empirical results show the following: (1) During the observation period, the average agricultural eco-efficiency exhibits a flat “M-shaped” fluctuating trend, a trend of fluctuant growth with phase characteristics, and the agricultural eco-efficiency of each county still has much room for improvement. (2) Significant spatial differences exist in agricultural eco-efficiency across counties. All of the studied counties, except for Nong’an, Huadian, Lishu, Yitong, Gongzhuling, and Qianguo, need to change the input and output structure to optimize agricultural eco-efficiency. (3) The panel data regression model verifies that the agricultural technology extension level, agricultural economic development level, agricultural industry structure, agricultural mechanization intensity and urbanization level have close correlations with agricultural eco-efficiency. (4) The research findings have important implications for policy makers formulating agricultural environmental policies in accordance with the local conditions of various counties.

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“…Eco-efficiency has been applied from many perspectives, such as the macro-economic [ 21 ], the meso-economic [ 22 ], and the micro-economic levels [ 23 ]. Liu et al [ 24 ] considered the provincial panel data in China during 1978–2017 to measure the agricultural eco-efficiency by the super-efficiency slacks-based measure (super-SBM) model. Peng et al [ 25 ] created a comprehensive evaluation index system, including undesirable outputs, and adopted SBM to analyze the characteristics and evolution of eco-efficiency at an individual tourism destination.…”

Section: Introductionmentioning

confidence: 99%

Mining Eco-Efficiency Measurement and Driving Factors Identification Based on Meta-US-SBM in Guangxi Province, China

Zuo

et al. 2021

IJERPH

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The mining industry is one of the pillar industries of Guangxi’s economic and social development. The output value of mining and related industries accounts for 27% of the whole district’s total industrial output value. Therefore, the mining eco-efficiency measurement in Guangxi can be of great significance for the sustainable development of Guangxi’s mining industry. This study adopted Meta-US-SBM to measure the mining eco-efficiency in Guangxi from 2008 to 2018, including economic efficiency, resource efficiency, and environmental efficiency. It used the standard deviation ellipse model to simulate the migration trend of four efficiencies in Guangxi and used GeoDetector and Tobit models to explore the internal and external factors that affect the mining eco-efficiency. The four efficiencies in Guangxi show large temporal and spatial heterogeneity, and the internal and external factors that affect the mining eco-efficiency are different. The following conclusions can be drawn. (1) Environmental efficiency and mining eco-efficiency are improving, while economic efficiency and resource efficiency are deteriorating. Cities bordering other provinces have a significantly better mining eco-efficiency than non-bordering cities. (2) The development center in Guangxi has migrated to the Beibu Gulf Economic Zone. (3) Natural resources index and mining economic scale have a great impact on the mining eco-efficiency, and with the increase of the mining economic scale, the mining eco-efficiency showed a typical “U-shaped” curve. Finally, this study put forward corresponding policy recommendations to improve the mining eco-efficiency in Guangxi from four aspects: opening-up, technological progress, regional coordination, and government control.

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Spatial-temporal characteristics and influencing factors of agricultural eco-efficiency in China in recent 40 years

Cited by 176 publications

References 54 publications

Smallholders’ Agricultural Production Efficiency of Conservation Tillage in Jianghan Plain, China—Based on a Three-Stage DEA Model

Smallholders’ Agricultural Production Efficiency of Conservation Tillage in Jianghan Plain, China—Based on a Three-Stage DEA Model

Evaluation and Influencing Factors of Agricultural Eco-efficiency in Jilin Agricultural Production Zone Based on Super-SBM and Panel Regression Methods

Mining Eco-Efficiency Measurement and Driving Factors Identification Based on Meta-US-SBM in Guangxi Province, China

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