Intelligence and Green Total Factor Productivity Based on China’s Province-Level Manufacturing Data

Zhang, Yining; Zhong, Wu

doi:10.3390/su13094989

Cited by 20 publications

(11 citation statements)

References 41 publications

(59 reference statements)

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“…Zhu Yingyu et al (2022) measured green total factor productivity based on the net carbon sink in China’s plantation sector using a stochastic frontier analysis with output-oriented distance function based on panel data from 30 Chinese firms from 2001 to 2019, and empirically examined the impact of agricultural mechanisation on green total factor productivity [ 20 ]. Based on provincial panel data from 2008 to 2017, Yining Zhang et al (2022) measured the green total factor productivity of China’s manufacturing industry using the Malmquist–Luenberger (ML) model and further constructed an empirical model to analyse the influence mechanism of green total factor productivity [ 21 ]. Chen Haisheng et al (2022) used the Slack-Based Model (SBM) Global Malmquist–Luenberger (GML) index to measure green total factor productivity in China’s agriculture by province and used social network analysis (SNA) and vector autoregressive model (VAR) impulse response function (IRF) to examine the green total.…”

Section: Indicator Construction Data Description and Measurement Mode...mentioning

confidence: 99%

Does the National Credit Demonstration Policy Affect Urban Green Economy Efficiency? Evidence from the Yangtze River Delta Region of China

Chen

Ni²,

Zhu

et al. 2022

IJERPH

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A more scientific green economy efficiency indicator is constructed based on OH (2010), and a multiperiod spatial DID model is used to examine the impact of national credit demonstration policies on urban green economy efficiency in a sample of cities above the prefecture level in the Yangtze River Delta. The study confirms the following: (1) The national credit demonstration policy makes a significant contribution to the green economic efficiency of cities, and it is conducive to strengthening awareness of the rule of law in the market to regulate market order. (2) The demand for credit regulation in coastal areas has increased under the new development pattern, and the national credit demonstration policy has effectively enhanced green economy efficiency through institutional supply. (3) Under the national credit demonstration policy, the subprovincial level and above can mobilise more resources for policy refinement and support, reducing transaction costs and improving the efficiency of the green economy. (4) The impact of the national credit demonstration policy on the efficiency of Zhejiang’s green economy is more obvious; but, under the overall framework of the Yangtze River Delta, the policy has a more prominent role in promoting green economy efficiency in other provinces. Policy insights are as follows: (1) Different cities have different degrees of impact on the efficiency of the green economy from the national credit demonstration policy, and they should implement differentiated measures based on regional heterogeneity; (2) regulating the use of administrative resources and avoiding undue administrative intervention are important prerequisites for promoting regional integration to enhance the efficiency of the green economy; and (3) strengthening interprovincial credit policy synergies can help to alleviate administrative distortions of policy implementation and enhance the efficiency of the regional green economy.

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Section: Indicator Construction Data Description and Measurement Mode...mentioning

confidence: 99%

Does the National Credit Demonstration Policy Affect Urban Green Economy Efficiency? Evidence from the Yangtze River Delta Region of China

Chen

Ni²,

Zhu

et al. 2022

IJERPH

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“…In their book Data Science Applied to Sustainability Analysis , Dunn and Balaprakash (2021) mention that data science and technology have become central to addressing sustainability challenges, and this role will only expand in the future [ 41 ]. In the era of Industry 4.0 artificial intelligence is important for fostering new competitive advantages, changing the energy consumption structure [ 42 ], promoting industrial transformation, as well as the advancement towards the middle and higher levels of the industrial value chain, which, in turn, exerts a significant influence upon sustaining the growth of China’s economy [ 43 ] and is one of the core factors for the development of GTFP. Artificial intelligence can influence GTFP in three ways: improving resource utilization in the production process, improving pollution treatment and pollution control, and fostering green industries and promoting green energy development [ 44 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of Artificial Intelligence on Regional Green Development under China’s Environmental Decentralization System—Based on Spatial Durbin Model and Threshold Effect

Fang

Cao

Sun

2022

IJERPH

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Artificial intelligence (AI) is the core technology of digital economy, which leads the transition to a sustainable economic growth approach under the Chinese-style environmentally decentralized system. In this paper, we first measured the green total factor productivity (GTFP) of 30 Chinese provinces from 2011 to 2020 using the super-efficiency slacks-based measure (SBM) model, analyzed the mechanism of the effect of AI on GTFP under the environmental decentralization regime, and secondly, empirically investigated the spatial evolution characteristics and the constraining effect of the impact of AI on GTFP using the spatial Durbin model (SDM) and the threshold regression model. The findings reveal: a U shape of the correlation of AI with GTFP; environmental decentralization acts as a positive moderator linking AI and GTFP; the Moran index demonstrates the spatial correlation of GTFP; under the constraint of technological innovation and regional absorptive capacity as threshold variables, the effect of AI over GTFP is U-shaped. This paper provides a useful reference for China to accelerate the formation of a digital-driven green economy development model.

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“…Zhang Yanan et al (2022) measured green total factor productivity in the Huaihe Economic Zone based on the carbon cycle in the period 2004-2017, and used a spatial Durbin model to analyse the effects of seven variables on green total factor productivity, including the level of economic development, environmental regulation, R&D level, and openness to the outside world [15]. Yining Zhang et al (2022) measured green total factor productivity in the Chinese manufacturing industry using the Malmquist-Luenberger (ML) model based on provincial panel data from 2008 to 2017, and further constructed an empirical model to analyse the impact mechanism of green total factor productivity [16]. Fang Lan et al (2022) used the SBM-GML index model to measure agricultural green total factor productivity based on provincial panel data in China from 2002 to 2015, and systematically examined the impact of crop insurance on agricultural green total factor productivity and its mechanism of action [17].…”

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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Intelligence and Green Total Factor Productivity Based on China’s Province-Level Manufacturing Data

Cited by 20 publications

References 41 publications

Does the National Credit Demonstration Policy Affect Urban Green Economy Efficiency? Evidence from the Yangtze River Delta Region of China

Does the National Credit Demonstration Policy Affect Urban Green Economy Efficiency? Evidence from the Yangtze River Delta Region of China

Impact of Artificial Intelligence on Regional Green Development under China’s Environmental Decentralization System—Based on Spatial Durbin Model and Threshold Effect

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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