Does digital technology innovation work better for industrial upgrading? An empirical analysis of listed Chinese manufacturing firms

Lo, Kai Lisa; Zhang, Jiaojiao; Xia, Fei

doi:10.1080/13504851.2022.2098238

Cited by 17 publications

(7 citation statements)

References 13 publications

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“…That is, when the labour force transfers from the primary industry to the secondary and tertiary industries, a country’s economy gradually evolves from the primary industry as the leading industry to the secondary and tertiary industries [ 70 ]. During industrial upgrading process, labour, capital, natural endowment, and technological progress have become important driving factors [ 71 , 72 , 73 ]. As a “living capital”, human capital, with labour as its carrier, has greater value-added potential than hard capital such as capital and material, and is more innovative and creative [ 21 , 53 ].…”

Section: Literature Review and Hypothesis Proposalmentioning

confidence: 99%

Does Human Capital Matter for China’s Green Growth?—Examination Based on Econometric Model and Machine Learning Methods

Liu

Cao

Fan

2022

IJERPH

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To tackle the increasingly severe environmental challenges, including climate change, we should pay more attention to green growth (GG), a path to realize sustainability. Human capital (HC) has been considered a crucial driving factor for developing countries to move towards GG, but the impact and mechanisms for emerging economies to achieve GG need to be further discussed. To bridge this gap, this paper investigates the relation between HC and GG in theory and demonstration perspective. It constructs a systematic theoretical framework for their relationship. Then, it uses a data envelopment analysis (DEA) model based on the non-radial direction distance function (NDDF) to measure the GG performance of China’s 281 prefecture level cities from 2011 to 2019. Ultimately, it empirically tests the hypothesis by using econometric model and LightGBM machine learning (ML) algorithm. The empirical results indicate that: (1) There is a U-shaped relationship between China’s HC and GG. Green innovation and industrial upgrading are transmission channels in the process of HC affecting GG. (2) Given other factors affecting GG, HC and economic growth contribute equally to GG (17%), second only to city size (21%). (3) China’s HC’s impact on GG is regionally imbalanced and has city size heterogeneity.

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Section: Literature Review and Hypothesis Proposalmentioning

confidence: 99%

Does Human Capital Matter for China’s Green Growth?—Examination Based on Econometric Model and Machine Learning Methods

Liu

Cao

Fan

2022

IJERPH

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“…The new digital information technology significantly promotes information dissemination and innovation, improves the information exchange efficiency of the entire production line, saves production resources, controls innovation costs, facilitates digital management of enterprises, and promotes the transfer of conventional manufacturing elements from the secondary to the tertiary sectors (Teece, 2018). Data from listed manufacturing companies in China demonstrate that the innovation of digital technology contributes to the manufacturing firms' industrial upgrading (Lo et al, 2022). And the digital economy in terms of G7 countries is a vital contributor to technological innovation according to Yuan et al (2021).…”

Section: Digital Economy Industrial Structure Upgrading and The Green...mentioning

confidence: 99%

“…For instance, in the steel industry, which accounts for the largest carbon emissions in the industrial sector, digital technology enables deep integration of data elements and modern industry, achieving intelligent control throughout the production line in terms of energy efficiency, quality, and environmental protection. And data modeling may be used to optimize processes using digital technologies like AI and big data, which reduces costs (Lo et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Digital technologies can also assist and guide more capital flow towards enterprises using lowcarbon technologies, effectively addressing the financing challenges in green industries. With characteristics such as spillovers and sharing, digital technologies allow companies to overcome temporal and spatial barriers to learn new technologies and knowledge globally, driving technological progress (Lo et al, 2022). Digital technologies optimize established industrial supply networks, value chains, and manufacturing processes, enhancing organizational operational effectiveness and thus driving industrial upgrading.…”

Section: Introductionmentioning

confidence: 99%

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Digital economy and the green transformation of manufacturing industry: evidence from Chinese cities

Wang,

Kang

2024

Front. Environ. Sci.

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The global economy is undergoing a transformative shift propelled by continuous technological advancements. This digital revolution has ushered in a new era characterized by the pervasive influence of the digital economy. Notably, the inherent “green” attributes of the digital economy, such as reduced marginal costs and diminished environmental impact, have injected fresh momentum into the green transformation of the industrial sector. Using spatial econometric model, we examine the impact of the digital economy on the green transformation of the manufacturing industry using panel data for 283 prefecture-level Chinese cities from 2011 to 2019. We first calculate the level of the manufacturing industry’s green transformation in this paper according to the Slack-Based Measure model. The green transformation of the industrial sector is facilitated by the digital economy in both the eastern and central regions, as revealed by heterogeneity analysis based on geographical areas. The moderating effect analysis reveals a distinct negative moderating impact of industrial structure upgrading and industrial agglomeration. Additionally, the threshold effect tests indicate significant nonlinear features in the influence of industrial structure upgrading on the green transformation of the manufacturing industry.

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“…(3) Only by continuously improving the level of intelligence in a region's manufacturing industry can we effectively ensure the development advantages of the urban manufacturing. Lo et al (2022) [17] found technology patents improve the total factor productivity of manufacturing enterprises by reducing operating costs, improving resource allocation efficiency, and enhancing innovation capabilities. Li et al (2022) [18] believed that big data-driven analysis, as one of the key technologies of artificial intelligence, can improve the market competitiveness of the manufacturing and help business leaders make wise decisions.…”

Section: The Concept Of Urban Advanced Manufacturing Developmentmentioning

confidence: 99%

The Evaluation Prediction System for Urban Advanced Manufacturing Development

Dou,

Sun,

Zhu

et al. 2023

Systems

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With the rapid development of the economy, it is important to reasonably evaluate the development status of the regional manufacturing industry. Given this, this article expands the evaluation indicators of urban advanced manufacturing (UAM) from the perspective of the push–pull-mooring (PPM). Then, it uses a machine learning (ML) method to predict the evaluation results of other cities through a small amount of sample data. The results show that: (1) From the current development status of UAM in Guangdong Province (GD), cities in the Pearl River Delta region occupy a dominant position. However, cities in eastern, western, and mountainous regions have strong development potential and lead cities. Therefore, each region has cities with high levels of development and has a demonstrative role. (2) By comparison, it was found that the overall development level of UAM in GD is not significantly different from that of the Yangtze River Economic Belt. However, due to significant differences in their extreme values, the proportion of cities above the average in the overall population is relatively small. This indirectly proves that GD’s UAM not only has a phased nature, but also has a demonstrative role. (3) The prediction effect of the perceptron model is better than other methods. Although neural network models have better prediction performance than other machine learning models, they should not overly rely on complex network structure prediction data. By comparing the results, the reliability is verified. Finally, according to the life cycle theory, we propose a targeted development path for different UAM.

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Does digital technology innovation work better for industrial upgrading? An empirical analysis of listed Chinese manufacturing firms

Cited by 17 publications

References 13 publications

Does Human Capital Matter for China’s Green Growth?—Examination Based on Econometric Model and Machine Learning Methods

Does Human Capital Matter for China’s Green Growth?—Examination Based on Econometric Model and Machine Learning Methods

Digital economy and the green transformation of manufacturing industry: evidence from Chinese cities

The Evaluation Prediction System for Urban Advanced Manufacturing Development

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