2023
DOI: 10.3389/fenvs.2022.1058664
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The impact of intelligent manufacturing on industrial green total factor productivity and its multiple mechanisms

Abstract: As an integration of artificial intelligence and advanced manufacturing technology, intelligent manufacturing has realized the innovation of manufacturing mode and created conditions for the green development of industry. After constructing a theoretical framework between intelligent manufacturing and industrial green total factor productivity, this paper uses panel data of 30 provinces in China from 2006 to 2020, and expresses the level of intelligent manufacturing with industrial robot density, to discuss th… Show more

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Cited by 34 publications
(24 citation statements)
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“…This method has been widely used in subsequent studies on the social effects of robots (Paul et al, 2020). Based on the common practice of the literature (Wang and Dong, 2020;Dauth et al, 2021;Chen et al, 2022;Ge and Zhao, 2023;Yang and Shen, 2023), this paper constructed a robot density index at the level of prefecture-level cities in China. First, International Federation of Robotics (IFR) industry classification data were matched with 14 two-digit industries in the industry classification of China's national economy.…”
Section: Core Explanatory Variablementioning
confidence: 99%
“…This method has been widely used in subsequent studies on the social effects of robots (Paul et al, 2020). Based on the common practice of the literature (Wang and Dong, 2020;Dauth et al, 2021;Chen et al, 2022;Ge and Zhao, 2023;Yang and Shen, 2023), this paper constructed a robot density index at the level of prefecture-level cities in China. First, International Federation of Robotics (IFR) industry classification data were matched with 14 two-digit industries in the industry classification of China's national economy.…”
Section: Core Explanatory Variablementioning
confidence: 99%
“…Bai introduced the interactive effect of individuals and time into the linear panel model to reflect the differences in common factors among individuals [86]. Compared with the traditional panel fixed effect model, the interactive fixed effect model fully considers the multidimensional shocks in the real world and the heterogeneity of different individuals' responses to these shocks, which can better reflect economic reality [87][88][89]. In order to better deal with the endogenous problem of the intermediary effect test equation, this paper uses an interactive fixed effect model to fit and calculate the regression coefficient and significance of the channel variables.…”
Section: Econometrics Modelmentioning
confidence: 99%
“…With the support of BDA, energy demand and use data can be spread quickly among enterprises, thereby promoting the rational allocation of energy within industries. As IIG expands, it could promote the formation of intelligent industrial clusters, thereby releasing the agglomeration effects of specialisation and diversification [48]. In these intelligent industrial clusters, information regarding energy prices, transaction volume, and inventory could flow freely, and energy efficiency could be improved.…”
Section: Analysis Of the Mechanisms Of Iig On Pccrmentioning
confidence: 99%
“…Since panel data include repeated observations over time, there could be cross-sectional correlations, heteroskedasticity, and serial correlations when fully biased results are obtained [63]. Because the Driscoll-Kraay (DK) estimators can be used in robustness tests to compensate for the shortcomings of the White-Rogers method, they are good at correcting for shortcomings in panel data and allow for more accurate estimates to be obtained [48,64]. Therefore, in the baseline regression model, we mainly used the DK estimators to estimate the model.…”
Section: Baseline Regressionmentioning
confidence: 99%