Environmental Regulation, Scientific and Technological Innovation, and Industrial Structure Upgrading in the Yellow River Basin, China

Wang, Jianshi; Cheng, Yu; Wang, Chengxin

doi:10.3390/ijerph192416597

Cited by 14 publications

(11 citation statements)

References 74 publications

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“…First, automated machines in manufacturing firms significantly reduce total employment. Column (1) of Table 3 shows that the effect of autom on lnl is significantly negative with a coefficient of -0.1690, indicating that manufacturing firms have a substitution effect on total employment, which is consistent with the findings of Wang (2014) [ 40 , 41 ], and Acemoglu and Restrepo (2018) [ 1 ]. This is due to the fact that firms invest in more automated machines, generating the effect of machine substitution on labor and thus reducing the demand for labor.…”

Section: Discussionsupporting

confidence: 85%

How automated machines influence employment in manufacturing enterprises?

Jiang,

Ge,

Yang

et al. 2024

PLoS ONE

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This paper theoretically analyzes and empirically examines the impact and mechanisms of automated machines on employment in manufacturing enterprises, drawing on task-based model and using micro data from listed Chinese manufacturing enterprises between 2012 and 2019. Our findings reveal that: (1) Automated machines in manufacturing enterprises leads to a substitution effect on the total labor force, with a substitution effect on low-skilled labor and a creation effect on high-skilled labor in terms of employment structure. (2) Further analysis indicates that automated machines primarily have a positive effect on R&D and technical staff, a non-significant effect on sales staff, and a negative impact on production, administrative, and financial staff. (3) The primary influencing mechanisms of automated machines on employment in manufacturing firms are productivity effects and output scale effects, based on the mediation effect model. (4) Considering the industry linkage effect, we employ the input-output method and the Input-Output Table and find that automated machines for upstream (downstream) manufacturing enterprises will result in a substitution effect on employment for downstream (upstream) enterprises. The novelties and research contributions are as follows: (1) we conduct a structural decomposition of total employment, and further decompose employment positions into production, R&D, sales, finance, and administration. (2) We try to investigate the industry linkage effect about the impact of automated machines on the employment of upstream and downstream enterprises. (3) We use data from listed manufacturing companies, and the data of existing research are about provincial and industry-level data.

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Section: Discussionsupporting

confidence: 85%

How automated machines influence employment in manufacturing enterprises?

Jiang,

Ge,

Yang

et al. 2024

PLoS ONE

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“…The findings demonstrate that both official and informal environmental regulation can have a positive and beneficial impact on industrial restructuring. Additionally, environmental regulation can also indirectly speed up industrial restructuring and transformation by setting technological progress as a mediating variable [4]. The research also finds that the growth and progress of green technology have played, to a certain degree, an intermediary role in the process of GF influencing ISU [30].…”

Section: The Mediating Role Of Technological Innovationmentioning

confidence: 85%

“…Recently, researchers have started to notice the correlation between environmental regulation and ISU. They argue that environmental regulation itself can directly affect ISU [4]. For example, the government imposes stricter environmental restrictions on high-emission industries, forcing them to carry out industrial renewal and optimization.…”

Section: Introductionmentioning

confidence: 99%

Research on Green Finance, Technological Innovation, and Industrial Structure Upgrading in the Yangtze River Economic Belt

Tang,

Yan,

Sheng

et al. 2023

Sustainability

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Along with the fact that China is in a critical period of economic structural transformation, industrial structural upgrading and transformation are imminent. The empirical research in this paper mainly constructs four groups of fixed effects models. The purpose is to investigate the impact mechanisms of green finance (GF), technological innovation (TI), and industrial structure upgrading (ISU) in 11 provinces (cities) along the Yangtze River Economic Belt (YREB) from 2011 to 2020. Based on the research method of the fixed effects model, this paper can analyze the association between GF, TI, and ISU in a more concise and intuitive way. The research conclusions mainly include the following three points. Firstly, both GF and TI are able to significantly contribute to the ISU of the YREB. Furthermore, GF can also promote TI to a certain extent. Second, TI serves as a partial intermediary in the process of GF’s favorable contribution to the ISU. In other words, green finance can act as an intermediary by enhancing technological innovation capacity so as to effectively accelerate the ISU of the YREB. Thirdly, this paper finds that there exists a degree of regional heterogeneity in the process of GF and TI affecting ISU in the YREB. For one thing, GF in the eastern part of the YREB can strongly accelerate the ISU. However, there is no obvious impact effect in the central and western regions. For another, TI in the eastern part of the YREB has, likewise, contributed positively to the ISU. In comparison, TI in the central region has a clearly negative effect on the ISU while there is no significant influence effect in the western region. In conclusion, this paper innovatively integrates GF, TI, and ISU into a framework for research. This paper not only widens the theoretical research domain of industrial structure upgrading but also provides practical guidance for the restructuring and transformation of industries in the YREB.

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“…Added value of the tertiary industry F [33] Proportion of output value of tertiary industry in GDP F [33] Ecological benefit Industrial nitrogen oxide emissions F [34] Industrial S02 emissions F [34] Industrial wastewater discharge F [34] Social benefit…”

Section: Economic Benefitmentioning

confidence: 99%

Research on the Evaluation of Regional Scientific and Technological Innovation Capabilities Driven by Big Data

Liang,

Wu,

Zhang

2024

Sustainability

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Scientific and technological innovation (STI) is an important internal driver of social and economic development. Reasonable evaluation of regional scientific and technological innovation (RSTI) capability helps discover shortcomings in the development of urban development and guides the allocation of scientific and technological resources and the formulation of policies to promote innovation. This paper analyzes new opportunities created by big data and artificial intelligence for the evaluation of RSTI capability, and based on this analysis, the collaborative evaluation schemes of multi-entity participation are investigated. In addition, considering the important value of unstructured data in evaluating STI, the Latent Dirichlet Allocation (LDA) topic model and sentiment analysis method are employed to analyze the construction of an evaluation indicator system that integrates scientific and technological news data. To fully utilize the respective advantages of human experts and machine learning in the field of complex issue evaluation, this paper proposes an RSTI capability evaluation model based on AHP-SMO human-machine fusion. This study promotes the integration of science and technology and economy and has theoretical and practical significance.

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Environmental Regulation, Scientific and Technological Innovation, and Industrial Structure Upgrading in the Yellow River Basin, China

Cited by 14 publications

References 74 publications

How automated machines influence employment in manufacturing enterprises?

How automated machines influence employment in manufacturing enterprises?

Research on Green Finance, Technological Innovation, and Industrial Structure Upgrading in the Yangtze River Economic Belt

Research on the Evaluation of Regional Scientific and Technological Innovation Capabilities Driven by Big Data

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