The Effect of Agricultural Environmental Total Factor Productivity on Urban-Rural Income Gap: Integrated View from China

Li, Qiang; Wu, Xiaohang; Zhang, Yi; Wang, Yafei

doi:10.3390/su12083327

Cited by 24 publications

(13 citation statements)

References 23 publications

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“…Wang et al, 2006), SBM directional distance function (B. Wang et al, 2010), and data envelopment analysis (DEA) (Q. Li et al, 2020; Mao & Sheng, 2012).…”

Section: Measurement Model Variable Definition and Data Descriptionmentioning

confidence: 99%

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Impact of China’s Local Government Competition and Environmental Regulation on Total Factor Productivity

et al. 2023

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In the transition of China’s economy toward high-quality development, it will be increasingly important to coordinate the relationship between local government competition, environmental regulation, and the transformation of the economic growth mode. To explore the impact of local government competition and environmental regulation on total factor productivity (TFP), in this study empirical analysis models were established using panel data of 30 provinces in China from 2001 to 2017. Local government competition was measured from the perspectives of foreign direct investment (FDI), fiscal expenditure, and tax. The results show that local government competition has a positive impact on the improvement in TFP. Taking environmental regulation into consideration, the impact of local government competition on TFP is weakened. Furthermore, under the current intensity of environmental regulation, local government fiscal expenditure competition and tax competition both lead to improvement in TFP, while FDI competition has the opposite effect. This paper explores the heterogeneity of the impact of local government competition and environmental regulation on TFP under different local government competition levels. Under the influence of environmental regulations, local government FDI competition has a negative impact on the improvement in TFP, particularly in “low” FDI competition areas. Local government fiscal expenditure competition has a positive impact on the improvement in TFP, particularly in “low” fiscal expenditure competition areas. The current level of local government tax competition has a negative impact on the improvement in TFP in “high” tax competition areas, whereas it has a positive impact on the improvement in TFP in “low” tax competition areas.

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“…Wang et al, 2006), SBM directional distance function (B. Wang et al, 2010), and data envelopment analysis (DEA) (Q. Li et al, 2020; Mao & Sheng, 2012).…”

Section: Measurement Model Variable Definition and Data Descriptionmentioning

confidence: 99%

“…Wang et al, 2010), and data envelopment analysis (DEA) (Q. Li et al, 2020;Mao & Sheng, 2012). Because DEA is a non-parametric method, the form of the production function is not required to be known in advance and complex assumptions are also not required.…”

Section: Variable Definitionmentioning

confidence: 99%

Impact of China’s Local Government Competition and Environmental Regulation on Total Factor Productivity

et al. 2023

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“…[18]. Li, Q. et al (2020) used the DEA-SBM super-efficiency model to measure the agricultural ETFP of 30 provincial regions in China [19]. Jacek, P. and Rafał, Ż.…”

Section: Literature Reviewmentioning

confidence: 99%

The Measurement and Influencing Factors of Total Factor Productivity in the Chinese Rural Distribution Industry

Ding

Zhang

2021

Sustainability

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Total factor productivity (TFP) is critical to the sustainable development of the rural distribution industry. Improvements in productivity of the rural distribution industry can promote the high-quality development of the Chinese distribution industry. Studying the characteristics and influencing factors of total factor productivity in regard to the rural distribution industry in China is significant for promoting the transformation and development of the rural distribution industry. This paper uses the DEA–Malmquist Index to measure the total factor productivity (TFP) of the Chinese rural distribution industry and its decomposition index, and uses a panel data model to empirically study its influencing factors. The results show that, from 2008 to 2018, the TFP of the Chinese rural distribution industry showed a trend of rising first and then fluctuating and declining, with an average annual growth rate of 2.93%; the fluctuation direction of the TFP of the rural distribution industry in the eastern and western regions of China is basically the same, which has had a reverse change relationship with the central and northeast regions for many years. The industrial structure, urbanization rate, rural informatization rate, and conditions of the transportation facilities have significant impacts on the TFP of the rural distribution industry, among which the informatization rate has the greatest positive impact.

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“…Qiang Li et al (2022) used a slack metric-based data envelopment analysis (DEA-SBM) super-efficiency model to measure agricultural environmental total factor productivity (ETFP) in 30 provinces and regions in China. Based on the measurement results, the impact of the urban-rural income gap on agricultural ETFP was empirically tested using a spatial autoregressive (SAR) model and estimation methods [29].…”

Section: Introductionmentioning

confidence: 99%

A Study of the Spatial Structure and Regional Interaction of Agricultural Green Total Factor Productivity in China Based on SNA and VAR Methods

Chen

Zhu

Sun³

et al. 2022

Sustainability

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As regional interaction increases in an open economy, a region’s green total factor productivity in agriculture must be considered alongside relationships with other regions. In this study, the slack-based model (SBM) global Malmquist–Luenberger (GML) index is used to measure the green total factor productivity of agriculture in each province of China, and the social network analysis (SNA) and vector autoregressive model (VAR) impulse response function (IRF) are used to examine the spatial network structure and regional interactivity. The research confirms that the absolute value and concentration of agricultural green total factor productivity are generally higher in the south than in the north of China, but the peak is lower in the south than in the north. The network density of agricultural green total factor productivity in China from 2008 to 2019 shows an increase, with the cut-off values of mean, 10, 50, and 100 treated as 4.97%, 2.57%, 3.30%, and 2.43%, respectively. From 2008 to 2019, the central potentials of network entry and network exit of green total factor productivity in China’s agriculture show a “V”-shaped and inverted “V”-shaped evolution path, respectively, with the density of cohesive subgroups growing, which demonstrates that the spatial structure of green total factor productivity in Chinese agriculture has experienced an evolutionary path from polycentric to monocentric to polycentric conditions. The spatial interaction of different cohesive subgroups is intensifying and has a certain degree of self-stability. In terms of regional interaction, the siphon effect of the east on the green development of agriculture in the central and western regions is significant, but the trickle-down effect is not obvious, and the interaction between the central and western regions has a catalytic effect on the efficiency of the green economy of agriculture in both regions. It is recommended that targeted policies be introduced to support the flow of agricultural factors and industrial division of labour between the central and western regions and the south and north, taking into account the actual situation. The novelty of this paper is that it focuses on the green total factor productivity of Chinese agriculture and combines the innovative use of the social network analysis paradigm to analyse the green development of agriculture in a country from a spatial dynamic evolutionary perspective. A limitation of the research methodology in this paper is its poor applicability to closed economy analysis.

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The Effect of Agricultural Environmental Total Factor Productivity on Urban-Rural Income Gap: Integrated View from China

Cited by 24 publications

References 23 publications

Impact of China’s Local Government Competition and Environmental Regulation on Total Factor Productivity

Impact of China’s Local Government Competition and Environmental Regulation on Total Factor Productivity

The Measurement and Influencing Factors of Total Factor Productivity in the Chinese Rural Distribution Industry

A Study of the Spatial Structure and Regional Interaction of Agricultural Green Total Factor Productivity in China Based on SNA and VAR Methods

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