Chasing Green: The Synergistic Effect of Industrial Intelligence on Pollution Control and Carbon Reduction and Its Mechanisms

Shen, Yang; Yang, Zhihong

doi:10.3390/su15086401

Cited by 11 publications

(4 citation statements)

References 63 publications

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“…Fan and Xin (2022) evaluated the electronic information MI at the value chain level by using analytic hierarchy process [64]. Shen and Yang (2023) used the bidirectional fixed effect model to evaluate the coordinated effect and mechanism of industrial digital intelligent innovation on enterprise green development [65].…”

Section: Dgi Evaluation Methodsmentioning

confidence: 99%

A systematic framework to improve the digital green innovation performance of photovoltaic materials for building energy system

Hu,

Sun,

Yin

et al. 2023

Environ. Res. Commun.

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Under the current ‘double carbon’ policy, the building materials manufacturing industry has seriously restricted the improvement of social and environmental benefits. Digital green innovation (DGI) in photovoltaic building materials enterprises (PBMES) plays a crucial role in solving the problems of high-quality environmental and economic development. In order to make the DGI of PBMES more effective, it is very critical to evaluate the performance of the DGI activities of PBMES. First, the evaluation index system is constructed. Then, it constructs the theoretical framework of the DGI performance evaluation of PBMES. After that, on the basis of combining various evaluation methods, a combination evaluation model based on compatibility and consistency was constructed and the convergence test and consistency test of the combination evaluation results were carried out by means of the variance method and Spearman rank correlation coefficient, which verified the scientificity and validity of the method. Finally, using the evaluation model, 16 PBMES were empirically studied. It reflected that the DGI performance evaluation index system of PBMES is composed of four indexes, namely, technology input, economic output, scientific and technological output, and social effect. The key factors affecting the DGI performance of PBMES are the investment in talent training, the proportion of digital technology in green products, the success rate of digital innovation product development increased by DGI cooperation, and the digital level of adopting environmental management system. This article combines consistency-based evaluation methods to not only obtain reasonable evaluation results, but also fully utilize multi-level methods to better describe the evaluation object. The means provided in this article are a new way to solve the DGI performance evaluation of PBMES.

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Section: Dgi Evaluation Methodsmentioning

confidence: 99%

A systematic framework to improve the digital green innovation performance of photovoltaic materials for building energy system

Hu,

Sun,

Yin

et al. 2023

Environ. Res. Commun.

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show abstract

“…Finally, the application of robots in various industries at the provincial level is summarized. The Bartik shift-share instrumental variable is now widely used to measure robot installation density at the city (province) level (Wu 2023;Yang and Shen, 2023;Shen and Yang 2023). The calculation process is as follows:…”

Section: Study Design and Data Sourcesmentioning

confidence: 99%

The impact of artificial intelligence on employment: the role of virtual agglomeration

Shen,

Zhang

2024

Humanit Soc Sci Commun

Self Cite

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Sustainable Development Goal 8 proposes the promotion of full and productive employment for all. Intelligent production factors, such as robots, the Internet of Things, and extensive data analysis, are reshaping the dynamics of labour supply and demand. In China, which is a developing country with a large population and labour force, analysing the impact of artificial intelligence technology on the labour market is of particular importance. Based on panel data from 30 provinces in China from 2006 to 2020, a two-way fixed-effect model and the two-stage least squares method are used to analyse the impact of AI on employment and to assess its heterogeneity. The introduction and installation of artificial intelligence technology as represented by industrial robots in Chinese enterprises has increased the number of jobs. The results of some mechanism studies show that the increase of labour productivity, the deepening of capital and the refinement of the division of labour that has been introduced into industrial enterprises through the introduction of robotics have successfully mitigated the damaging impact of the adoption of robot technology on employment. Rather than the traditional perceptions of robotics crowding out labour jobs, the overall impact on the labour market has exerted a promotional effect. The positive effect of artificial intelligence on employment exhibits an inevitable heterogeneity, and it serves to relatively improves the job share of women and workers in labour-intensive industries. Mechanism research has shown that virtual agglomeration, which evolved from traditional industrial agglomeration in the era of the digital economy, is an important channel for increasing employment. The findings of this study contribute to the understanding of the impact of modern digital technologies on the well-being of people in developing countries. To give full play to the positive role of artificial intelligence technology in employment, we should improve the social security system, accelerate the process of developing high-end domestic robots and deepen the reform of the education and training system.

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“…At the enterprise level, AI facilitates optimal resource allocation, thereby enhancing productivity, cost savings, and sustainable development conditions through the analysis of extensive data, the identification of emerging technological trends, and guidance for investments in green technologies. At the industry chain level, AI's innovative characteristics allow for the clustering and reorganization of innovation elements, leading to synergistic configuration with other factors and expediting the improvement of green technology within the industry chain itself [48]. This improvement not only promotes the application of green technologies but also generates spillover effects that propel the entire industry chain in a greener and more sustainable direction [49].…”

Section: Direct Impactsmentioning

confidence: 99%

How Does Artificial Intelligence Impact Green Development? Evidence from China

Chen,

Wang,

Wang

2024

Sustainability

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Artificial intelligence not only changes the production methods of traditional industries but also provides an important opportunity to decouple industrial development from environmental degradation and promote green economic growth. In order to further explore the green value of AI, this paper constructs an indicator of industrial robot penetration at the regional level, based on the idea of Bartik’s instrumental variable, and measures green development efficiency using the improved Super-SBM model. Based on a comprehensive explanation of the influence mechanism, a spatial measurement model and mediating effect model are constructed to test the spatial spillover effect and transmission mechanism between AI and green development. This study shows that (1) there is a significant inverted U shape in the impact of AI on green development; (2) the heterogeneity analysis finds that the structural dividend of AI is more obvious in capital-intensive and technology-intensive areas, which can more fully release its empowering effect on green development; (3) AI can not only directly affect green development but also indirectly affect green development by promoting green technology innovation and optimizing industrial structures, etc.; (4) AI has a significant inverted U-shaped spatial spillover effect on green development, and the development of local AI has a radiation-driven effect on the green development performance of its spatially related areas. The research methodology of this paper can be used for future research, and the results could provide support for the formulation of regional AI applications and green development policies.

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Chasing Green: The Synergistic Effect of Industrial Intelligence on Pollution Control and Carbon Reduction and Its Mechanisms

Cited by 11 publications

References 63 publications

A systematic framework to improve the digital green innovation performance of photovoltaic materials for building energy system

A systematic framework to improve the digital green innovation performance of photovoltaic materials for building energy system

The impact of artificial intelligence on employment: the role of virtual agglomeration

How Does Artificial Intelligence Impact Green Development? Evidence from China

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