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

Abstract

The application of intelligent technology has an important impact on the green total factor productivity of China’s manufacturing industry. Based on the provincial panel data of China’s manufacturing industry from 2008 to 2017, this article uses the Malmquist–Luenburger (ML) model to measure the green total factor productivity of China’s manufacturing industry, and further constructs an empirical model to analyze the impact mechanism of intelligence on green total factor productivity. The results show that intelligence can increase the green total factor productivity of the manufacturing industry. At the same time, mechanism analysis shows that intelligence can affect manufacturing green total factor productivity by improving technical efficiency. However, the effect of intelligence on the technological progress of the manufacturing industry is not significant. In addition, the impact of intelligence has regional heterogeneity. It has significantly promoted the green total factor productivity in the eastern and central regions of China, while its role in the western region is not obvious. The research in this article confirms that intelligence has a significant positive impact on the green total factor productivity of the manufacturing industry, and can provide suggestion for the current further promotion of the deep integration of intelligence and the green development of the manufacturing industry to achieve the strategic goal of industrial upgrading.

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

“…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.…”

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

“…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 ].…”

Influence Mechanism of Industrial Agglomeration and Technological Innovation on Land Granting on Green Total Factor Productivity

“…The research into green total factor productivity at home and abroad mainly focuses on measurement methodologies, such as the SBM-ML model [2], the trans-log production function [3], the Divisia-Luenberger productivity Index [4], the modified Malmquist-Luenberger index based on directional distance function [5], the non-radial directional distance function [6] and the Global Malmquist-Luenberger index [7]. It also takes into account the interactions with affecting elements, such as intelligence [8], emission trading [9], land marketing [10], outward foreign direct [11], and market integration [12]. Theoretical levels of land giving behavior and economic impact [13][14][15], as well as the influence of land transfer marketization on land use efficiency and the environment [16,17], are among the existing areas of study on the impact of land granting on GTFP.…”