Measurement of green total factor productivity on Chinese laying hens: From the perspective of regional differences

Junzhi, Li; Li, Junwei; Sun, Zhenlei; Zhong, Shen

doi:10.1371/journal.pone.0255739

Cited by 4 publications

(3 citation statements)

References 56 publications

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“…The average annual growth rates of the GTFP of small-, medium-, and large-scale dairy farming is 1.98%, 2.73%, and 3.27%, respectively. Li, J., et al ( 2021) [74] The value of green total factor productivity on Chinese laying hens in Zhejiang province and Jiangsu province is 1.00958 and 1.00397, respectively.…”

Section: Our Resultsmentioning

confidence: 99%

Impact of Environmental Regulation on the Green Total Factor Productivity of Dairy Farming: Evidence from China

Liu

Cui

2022

Sustainability

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Environmental regulation is essential to promote green and sustainable development in dairy farming. Nevertheless, limited studies have focused on the impact of environmental regulation on the green total factor productivity (GTFP) of dairy farming. This study measures the GTFP of dairy farming in 27 provinces in China during 2009–2020 using the Slack Based Measure (SBM) model and the Malmquist–Luenberger (ML) productivity index. In addition, random effects and threshold regression models are used to measure the impact of environmental regulations on the GTFP of dairy farming. The results demonstrate the fluctuating growth of the GTFP of dairy farming and that technical efficiency is the primary driver of the GTFP growth. The annual growth rate of GTFP is the highest in large-scale dairy farming (3.27%), followed by medium-scale dairy farming (2.73%) and small-scale dairy farming (1.98%). Furthermore, environmental regulation positively affects the GTFP and has a threshold effect on the GTFP, with the urban–rural income gap as the threshold variable in medium-scale dairy farming and small-scale dairy farming. The impact on the GTFP can be significantly negative if the urban–rural income gap crosses the threshold value. Overall, this study provides some policy recommendations for attaining green and sustainable dairy farming development in China.

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Section: Our Resultsmentioning

confidence: 99%

Impact of Environmental Regulation on the Green Total Factor Productivity of Dairy Farming: Evidence from China

Liu

Cui

2022

Sustainability

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“…Swiecki [33] used wage differentials between industries to measure the degree of factor distortion. Li et al [34] used the direct distance function (DDF)-Data Envelopment Analysis (DEA) model to measure the green total factor productivity of laying hens in China.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on the income distribution effect of factor allocation efficiency changes among China’s three major industries

Zhang,

Dong,

Yao

2023

PLoS ONE

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This paper aims to study the impact of correcting the factor misallocation among China’s three major industries on China’s income gap and income distribution pattern. By using the industry Panel data at the provincial level in China, we measure the degree of factor misallocation among the three major industries in China’s provinces from 2002 to 2019 by building a factor misallocation measurement model, and then uses reverse thinking to compare the income gap under the condition of no factor misallocation with the actual income gap, and then obtains the impact of factor misallocation on the income gap, And use this method to focus on analyzing the impact of factor allocation efficiency changes among the three industries on income distribution pattern. The research finds that: (1) There is a serious factor misallocation among the three major industries in each province. From the perspective of subdivided factors, the factor misallocation among the three major industries in China’s provinces is mainly caused by labor misallocation. Factor misallocation shows a trend of convergence first and then divergence among regions. (2) There is a strong heterogeneity in the explanatory power of different dimensions of the income gap of factor misallocation among the three major industries in China’s provinces. Among them, correcting the misallocation of total factors among the three major industries in China’s provinces can only narrow the internal income gap of the tertiary sector of the economy, and expand the internal income gap between the primary and secondary industries. (3) The impact of correcting the total misallocation, capital misallocation and labor misallocation among the three industries on the income gap among industries or provinces is narrowing first and then expanding. (4) Further research shows that although the level of factor misallocation among provinces in China is significantly lower than the average level of factor misallocation among the three major industries within each province, it has a stronger explanatory power for the inter-provincial income gap. Correcting the total factor misallocation and labor misallocation among provinces can significantly reduce the inter-provincial income gap in China. Correcting the total factor misallocation and labor misallocation among provinces in 2019 can reduce the inter-provincial income gap by 51.48% and 81.68% respectively. Only correcting the capital misallocation among provinces will expand the inter-provincial income gap, and only correcting the capital misallocation among provinces in 2019 will expand the inter-provincial income gap by 112.21%.

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“…Shi and Li (2019) used MML productivity index to evaluate GTFP of China's manufacturing. Based on the SBM model, Li J. et al (2021) constructed MML index to comprehensively access GTFP on Chinese laying hens (GTCL). A quantitative analysis of the changes in industrial GTFP in the Yangtze River Economic Zone was performed by Zhang et al (2022) using the MML index which takes into account expected and unexpected output.…”

Section: Temporal Heterogeneitymentioning

confidence: 99%

How big data drives green economic development: Evidence from China

Wang

Huang

Wang

2022

Front. Environ. Sci.

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Big data can improve production efficiency and optimize resource allocation, which is conductive to achieve sustainable development. This paper explores the impact of big data on green economic development. The global MINDW-MML model is used to measure green economic development and text analysis method is used to quantify the development of big data. An empirical study is conducted based on the panel data from 30 provinces in China from 2011 to 2018. Results show that, big data promotes the development of green economy and plays a greater role in facilitating technological progress than improving efficiency. As for sub-indicators of big data, cloud computing, Internet of things, artificial intelligence, and Hadoop positively affect technological progress, while blockchain can improve efficiency. In addition, the positive role of big data in promoting green technological progress and green efficiency will vary according to geographical location, the intensity of environmental governance and the development of digital financial inclusion. As moving into the good phase of the economy, big data is more inclined to enhance green technological progress, while in a sluggish phase, it improves green efficiency more. These findings point the way forward for sustainable development. The Chinese government can actively build information infrastructure and improve the technical level and application capacity of big data.

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Measurement of green total factor productivity on Chinese laying hens: From the perspective of regional differences

Cited by 4 publications

References 56 publications

Impact of Environmental Regulation on the Green Total Factor Productivity of Dairy Farming: Evidence from China

Impact of Environmental Regulation on the Green Total Factor Productivity of Dairy Farming: Evidence from China

Research on the income distribution effect of factor allocation efficiency changes among China’s three major industries

How big data drives green economic development: Evidence from China

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