Analysis of the spatial characteristics and influencing factors of agricultural eco-efficiency: evidence from Anhui Province, China, during the period 2011–2018

Wu, Yimin; Rahman, Rosmanjawati Abdul; Yu, Qiuju

doi:10.1007/s10661-022-09817-9

Cited by 13 publications

(6 citation statements)

References 28 publications

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“…This study found that the average AEE in Liaoning Province increased by 13.55% from 2014 to 2020. This shows that AEE is increasing, consistent with the conclusions of relevant studies at the national and provincial levels [48,49]. Compared with previous studies [50], to further analyze the impact of the agricultural economic level on AEE, this study grouped and further compared according to the income situation and discussed the differential impact of different factors on AEE at different income levels.…”

Section: Discussionsupporting

confidence: 82%

Measurement of Agricultural Eco-Efficiency and Analysis of Its Influencing Factors: Insights from 44 Agricultural Counties in Liaoning Province

Zhang,

Jin

2024

Land

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Agricultural eco-efficiency (AEE) considers economic and environmental benefits and is a key indicator of green agricultural development. To achieve the multiple goals of improving agricultural production efficiency, reducing agricultural environmental damage, and reducing the input of agricultural resources, this study enriches the case study of agricultural production performance evaluation at the county level by measuring the AEE of 44 agricultural counties in Liaoning Province based on panel data and a super-efficient slacks-based measure model including undesired outputs. A two-way fixed-effects model was used to analyze the impact of agricultural development, macro-environment, and policy support on AEE. We found that the average AEE of the counties in Liaoning Province in 2014, 2016, 2018, and 2020 was 0.716, 0.735, 0.749, and 0.813, respectively, indicating a cumulative improvement rate of 13.55%. The average AEE levels gradually improved during the study period. Notably, the development of AEE among the counties was uneven. AEE was distributed in a “block-like” manner, and its local correlation presents a phenomenon of “small agglomeration and large dispersion”. In addition, the level of the agricultural economy, industrialization, and urbanization significantly promoted the improvement of AEE, and the promoting effects varied between different income levels and regions. Therefore, Liaoning Province needs to improve the AEE of each county according to local conditions and narrow the differences in AEE between counties. To continuously improve the level of rural economic development, lead the development of agricultural modernization with new urbanization, and comprehensively improve the overall AEE of counties. The research results are of guiding significance for deepening the study of AEE and can provide decision-making support for optimizing the mode of agricultural production and promoting the green development of regional agriculture.

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Section: Discussionsupporting

confidence: 82%

Measurement of Agricultural Eco-Efficiency and Analysis of Its Influencing Factors: Insights from 44 Agricultural Counties in Liaoning Province

Zhang,

Jin

2024

Land

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“…Chen et al Measuring agricultural productivity from an environmental constraint perspective using agricultural surface source pollution as a non-desired output (Chen et al, 2022). Also using agricultural surface pollution as a non-desired output are (Wu et al, 2022). One is the inclusion of both carbon emissions and surface source pollution in nondesired outputs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Combined with existing literature ( Liu et al, 2021;Li & Liao, 2022;Luo et al, 2023;Wu et al, 2022;, The following control variables were selected: the level of agricultural development (expressed as the total output value of agriculture, forestry, animal husbandry and fishery per capita, AGDP), the level of agricultural agglomeration (calculated as the location quotient, ALQ), the level of urbanisation (expressed as the ratio of urban population to total population, UR) and the disposable income per rural resident (AI). In order to exclude the influence of price factors, the variables of agricultural development level and rural residents' per capita disposable income are deflated using 2007 as the base period.…”

Section: β Convergence Characteristics Of Agtfp In Xinjiangmentioning

confidence: 99%

Spatiotemporal Patterns and Convergence Analysis of Green Agricultural Development: A Case Study of Xinjiang in China

Xia,

Ma,

Gao

et al. 2024

Preprint

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Under the dual historical responsibility of the double carbon target and the rural revitalisation strategy, effectively improving green agricultural development level in Xinjiang is an important means to break the dilemma of inclusive agricultural economic growth under the complex environmental regulations in the western region nowadays. Based on the panel data of Xinjiang from 2007 to 2019, this paper incorporates agricultural carbon emissions and agricultural non-point source pollution into the same framework. The SBM-Global-Malmquist-Luenberger(GML) productivity index including directional distance function is used to measure the agricultural green total factor productivity(AGTFP) in various regions of Xinjiang, and then the Dagum Gini coefficient decomposition and kernel density estimation are used to investigate the regional differences and dynamic evolution of agricultural green development level in Xinjiang. Finally, the classical and spatial β-convergence models are used to analyze the convergence characteristics and influencing factors of agricultural green development level in Xinjiang. The study found that: AGTFP in Xinjiang showed a "wave-like" development during the sample period, with an overall growth trend; There are significant non-equilibrium characteristics of AGTFP in the sample period, and the agglomeration phenomenon of AGTFP gradually strengthens over time; The overall variation in AGTFP shows a fluctuating upward trend, with intra-regional variation being greater in northern than in southern and eastern Xinjiang, and inter-regional variation making the highest contribution to the overall variation; Inter-regional differences AGTFP have gradually widened and are multi-polar. Polarisation has increased in the northern and southern Xinjiang regions, and the gap has decreased in the eastern Xinjiang region; There is significant absolute β convergence and conditional β convergence of AGTFP during the sample period, and the rate of conditional β convergence is significantly higher than that of absolute β convergence, and the inclusion of spatial factors further increases the rate of convergence. There is still much room for improvement in the level of green agricultural development in Xinjiang. We should speed up green agricultural technology innovation and improve AGTFP; formulate green agricultural development policies according to local conditions; pay attention to the convergence effect and promote coordinated growth in AGTFP in regional agriculture.

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“…Then, the relationship between aggregate data and system dynamics is

where I and j belong to natural numbers. Suppose that the cluster data information is key , I is the industry of the cluster data source (monuments = 1, nature = 2, and humanities = 3) [ 18 ], j is the type of cluster data (structured cluster data = 1, semi-structured = 2, and unstructured = 3), and k is the processing method of cluster data (industry standard = 1, relevant industry standard = 2, and this industry standard = 3); then, I is described as c i I , j , k , I , j , k ∈ (1,2,….., n ), and n is a nonzero natural number. The fusion function φ ( x ) is used to calculate the fusion degree [ 19 ].…”

Section: Research Modementioning

confidence: 99%

An Optimization Analysis Model of Tourism Specialized Villages Based on Neural Network and System Dynamics

Wang

Sui-ying

et al. 2022

Computational Intelligence and Neuroscience

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With the rapid development of tourism, professional tourism villages emerge one after another, which has become the focus of the tourism industry. At present, there are some problems in tourism professional villages, such as imperfect management and inaccurate prediction of future development, which affect the rational allocation of tourism resources. In order to improve the distribution of tourism resources and better predict the development of tourism professional villages, it is necessary to make comprehensive judgment and analysis, especially the analysis of indicators such as the prediction and development judgment of tourism professional villages. This paper discusses the optimization analysis of the agglomeration of tourism specialized villages by backpropagation (BP) neural network and system dynamics model, analyzes the system structure of the agglomeration factors of tourism specialized villages, and promotes the intelligent integration of the agglomeration factors. The development of clusters of professional villages promotes data integration among resources, economy, society, and other elements and presents the characteristics of big data. As the level of concentration of professional villages increases, the complexity of the associated factors also increases, which increases the difficulty and effectiveness of tourism analysis. In view of this situation, taking mountain tourism as the research object, this paper proposes an improved system dynamics model based on BP, extracts features from cross factor (resource, economic, and social) data, and optimizes the relationship between professional village agglomeration and various factors. The MATLAB simulation results show that based on the improved system dynamics analysis, the simplification rate of (resources, economy, and society) data can be controlled at more than 24%, the degree of agglomeration is more than 95%, and the construction time of the relationship map of related factors is less than 40 s. Therefore, the analysis method proposed in this paper is suitable for the calculation of the agglomeration of tourism professional villages in the mountain area and can meet the needs of the development of tourism professional villages in the mountain area.

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Analysis of the spatial characteristics and influencing factors of agricultural eco-efficiency: evidence from Anhui Province, China, during the period 2011–2018

Cited by 13 publications

References 28 publications

Measurement of Agricultural Eco-Efficiency and Analysis of Its Influencing Factors: Insights from 44 Agricultural Counties in Liaoning Province

Measurement of Agricultural Eco-Efficiency and Analysis of Its Influencing Factors: Insights from 44 Agricultural Counties in Liaoning Province

Spatiotemporal Patterns and Convergence Analysis of Green Agricultural Development: A Case Study of Xinjiang in China

An Optimization Analysis Model of Tourism Specialized Villages Based on Neural Network and System Dynamics

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