A multi-hierarchy meta-frontier approach for measuring green total factor productivity: An application of pig breeding in China

Zhong, Shen; Li, Junwei; Chen, Xi; Wen, Hongmei

doi:10.1016/j.seps.2021.101152

Cited by 15 publications

(8 citation statements)

References 60 publications

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“…1. From 2004 to 2018, China's large-scale GTPB was the highest, followed by medium-sized GTPB and small-scale GTPB [62]. There is a close relationship between GTPB and breeding scale.…”

Section: Conclusion and Policy Implicationsmentioning

confidence: 96%

Analysis on the green total factor productivity of pig breeding in China: Evidence from a meta-frontier approach

Zhong

Guo

2022

PLoS ONE

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The pig industry occupies an extremely significant position in agriculture. The input cost, output income and the amount of pollution emitted by pig farming of different scales are unequal. It is of great practical importance to reduce pollutant emission by improving efficiency for the development of hog breeding industry in China. With the addition of undesirable output, this paper uses the Slack Based Measure- Metafrontier Malmquist Luenberger index model considering scale heterogeneity to explore the evolution characteristics of China’s green total factor productivity of pig breeding (GTPB) based on the data of China’s 17 major pig producing provinces from 2004 to 2018. The results indicate that: (1) From 2004 to 2018, China’s large-scale GTPB is the highest, the medium-sized is the second, and the small-scale is the lowest. (2) In terms of regional distribution, China’s GTPB in western region is the highest, in eastern region is the second, and in central region is the lowest. (3) China’s GTPB shows efficiency growth and technological decline from 2004 to 2018. The pig breeding industry is generally fragile, which is greatly affected by emergencies. (4)The TGR of large-scale pig breeding is closest to 1, followed by middle-scale, and finally small-scale. According to the above empirical results, this text puts forward some policy suggestions to improve GTPB and environmental protection recommendations of hog breeding.

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“…1. From 2004 to 2018, China's large-scale GTPB was the highest, followed by medium-sized GTPB and small-scale GTPB [62]. There is a close relationship between GTPB and breeding scale.…”

Section: Conclusion and Policy Implicationsmentioning

confidence: 96%

Analysis on the green total factor productivity of pig breeding in China: Evidence from a meta-frontier approach

Zhong

Guo

2022

PLoS ONE

Self Cite

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“…Onyenweaku and Effiong (2006) utilized the SFA method to measure pig productivity in Nigeria and investigate the factors influencing it. Common non-desired output indicators include measures of chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) surface source pollution values for PGTFP (Jia et al, 2021;Zhong et al, 2022a).…”

Section: Literature Review and Theoretical Discussionmentioning

confidence: 99%

Regional differences and convergence of green total factor productivity in pig breeding: evidence from China

Wang,

Chang,

Kong

2023

Front. Environ. Sci.

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Achieving green, healthy, and cyclical development in China’s pig industry not only enables the adjustment of the rural industrial structure but also helps meet community demand for safe meat, thus playing a critical role in promoting sustainable agricultural development. This research utilizes panel data from 21 provinces (districts) in China from 2004 to 2021 to construct an evaluation system for green total factor productivity in pig breeding (PGTFP). The super-efficient slacks-based measure (SBM) model is employed to measure PGTFP. Conversely, regional disparities in PGTFP are determined using the Dagum Gini coefficient method. Furthermore, we analyzed the convergence of pig breeding in different regions. The findings indicate that PGTFP in China exhibits fluctuations, with the highest PGTFP, observed in areas of constrained development. Development differences in PGTFP across China have gradually diminished, with the greatest development differences observed within potential growth areas. Furthermore, disparities between focused development areas and potential growth areas are the most pronounced and are primarily attributable to the super-variable density. Both α- and β-convergence are observed for PGTFP at the national level and within each breeding area. However, the α-convergence pattern during COVID-19 is not obvious. Consequently, the formulation of differentiated farming development strategies and fostering the coordinated development of pig breeding in all regions have become imperative. Meanwhile, preparing contingency prevention measures to promote stable and high-quality pig breeding is necessary.

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“…In animal husbandry, DEA has been extensively used to measure the environmental efficiency of pig breeding (Galanopoulos et al, 2006). Especially in China, with the transformation of the hog industry, environmental externalities have become a very serious problem (Kuhn et al, 2020), and some studies have added negative output into the evaluation system of pig breeding efficiency (Kuhn et al, 2020;Zhong et al, 2021). Furthermore, many studies have used the SBM-DEA model with negative output to investigate environmental efficiency (Yao et al, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Evolution trend and promotion potential of environmental efficiency of dairy farming in china from the perspective of “club convergence”

Wang²,

Wang³

et al. 2022

Front. Environ. Sci.

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There are differences in resource endowments, environmental regulations, and production functional areas among different regions in China, which makes dairy farming have certain heterogeneity and imbalance in pollution control, which is mainly reflected in the differences in the environmental efficiency of dairy farming among regions. By analyzing the convergence trend of the dairy farming environment in different regions and scales, it is helpful to predict the potential of improving environmental efficiency, and promote the high-quality development of dairy farming. Based on the dairy farming data of 27 provinces (cities and districts) in China from 2004 to 2019, the SBM model of unexpected output was used to measure the environmental efficiency of dairy farming on different scales. It was found that there were certain differences in the environmental efficiency of dairy farming in China. The overall environmental efficiency of dairy farming showed a “U" evolution trend, including small-scale, medium-scale, and large-scale. The average values of their environmental efficiency were 0.6859, 0.6930, and 0.6855 respectively, and the environmental efficiency of medium-scale dairy farming was the best. The convergence analysis of environmental efficiency showed that there was a conflict between the traditional σ convergence and β convergence results. However, the club convergence showed that the environmental efficiency of small-scale dairy farming converged in general, which could be further divided into two club convergence. The environmental efficiency of medium-scale and large-scale dairy farming did not converge in general but did converge in four different clubs, respectively. Taking the highest environmental efficiency value in the “club” as the reference, the potential to improve the environmental efficiency of dairy farming in different sizes was calculated. The prediction results showed that the gap between the actual value and the potential value was narrowing. It is expected that the potential to improve the environmental efficiency of dairy farming in China will remain above 10% in the next 10–20 years. Based on the above studies, this paper put forward some policy suggestions, such as changing the development direction of dairy farming, changing scale expansion to structural upgrading, implementing differentiated environmental efficiency improvement strategies, narrowing the efficiency gap between regions, paying attention to the input-output structure of factors, and improving the efficiency of breeding environment from the perspective of “eating” and " emission ".

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A multi-hierarchy meta-frontier approach for measuring green total factor productivity: An application of pig breeding in China

Cited by 15 publications

References 60 publications

Analysis on the green total factor productivity of pig breeding in China: Evidence from a meta-frontier approach

Analysis on the green total factor productivity of pig breeding in China: Evidence from a meta-frontier approach

Regional differences and convergence of green total factor productivity in pig breeding: evidence from China

Evolution trend and promotion potential of environmental efficiency of dairy farming in china from the perspective of “club convergence”

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