Can artificial intelligence achieve carbon neutrality? Evidence from a quasi-natural experiment

Chen, Siqi; Zhang, Shuyunfan; Zeng, Qingxuan; Ao, Jiaxuan; Chen, Xiaohua; Zhang, Shizhao

doi:10.3389/fevo.2023.1151017

Cited by 6 publications

(2 citation statements)

References 67 publications

(81 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AI technology is used in the manufacturing industry to reduce carbon emissions intensity by improving energy efficiency. Simultaneously, intelligent transformation breaks the restrictions of traditional industries in terms of time and space; contributes to the efficient reorganization and rational allocation of manpower, capital, and resources [25]; enables business transformations [26]; drives the rationalization of the industrial structure and structural upgrading; and is conducive to the realization of carbon neutrality. Second, as a digital tool, AI technology can help realize low carbon development in terms of both classification and prediction.…”

Section: Influence Of Enterprise Ai Technology On Carbon Emissionsmentioning

confidence: 99%

Artificial Intelligence and Carbon Emissions in Manufacturing Firms: The Moderating Role of Green Innovation

Chen,

Jin

2023

Processes

View full text Add to dashboard Cite

Carbon emissions have gained worldwide attention in the industrial era. As a key carbon-emitting industry, achieving net-zero carbon emissions in the manufacturing sector is vital to mitigating the negative effects of climate change and achieving sustainable development. The rise of intelligent technologies has driven industrial structural transformations that may help achieve carbon reduction. Artificial intelligence (AI) technology is an important part of digitalization, providing new technological tools and directions for the low carbon development of enterprises. This study selects Chinese A-share listed companies in the manufacturing industry from 2012 to 2021 as the research objects and uses a fixed-effects regression model to study the relationship between AI and carbon emissions. This study clarifies the significance of enterprise AI technology applications in realizing carbon emissions reduction and explores the regulatory mechanism from the perspective of the innovation effect. The results show that the application of enterprise AI technology positively impacts carbon emissions reduction. Simultaneously, green technological innovation, green management innovation, and green product innovation play moderating roles; in other words, enterprise green innovation strengthens the effect of AI on carbon emissions reduction. This study clarifies the necessity of intelligent manufacturing and enriches theories related to AI technology and carbon emissions.

show abstract

Section: Influence Of Enterprise Ai Technology On Carbon Emissionsmentioning

confidence: 99%

Artificial Intelligence and Carbon Emissions in Manufacturing Firms: The Moderating Role of Green Innovation

Chen,

Jin

2023

Processes

View full text Add to dashboard Cite

show abstract

“…lndroad Natural logarithm of the density of roads and railways (miles per square kilometre)(Chen et al, 2023) …”

mentioning

confidence: 99%

Investigating the nonlinear carbon reduction effect of AI: empirical insights from China’s provincial level

Xie,

Wang

2024

Front. Environ. Sci.

View full text Add to dashboard Cite

In the context of rapid advancement in automation and increasing global warming, understanding the impact of artificial intelligence (AI) on carbon emissions (CES) is a cutting-edge research topic. However, there is limited focus in existing research on the nonlinear carbon reduction effect (CRE) of AI. This paper first theoretically elaborates the dual impact mechanisms of AI on CES and illuminates the nonlinear carbon reduction mechanisms of AI. Then, this study employs panel data encompassing 30 Chinese provinces between 1997 and 2019 to empirically test the net effect of AI on CES and the nonlinear carbon reduction effect of AI through econometric models. The results are as follows: first, although AI can both reduce and increase CES, AI primarily helps decrease CES. This conclusion holds true even after considering robustness, endogeneity, and spatial heterogeneity. Secondly, relative to the central and western regions, AI has significant achievement in reducing carbon intensity and per capita CES in the eastern region. However, there is still room for improvement in terms of reducing the total CES in the eastern region. Thirdly, improving the AI development level (AIDL) can magnify the marginal CRE of AI and lead to a nonlinear CRE of AI. Lastly, even if the AIDL remains constant, improving the level of marketization, human capital, digital infrastructure, economic development, openness, and government intervention can also amplify the marginal CRE of AI and lead to a nonlinear CRE of AI. To fully harness the potential of AI for green development, concerted efforts should be directed towards enhancing the innovation and application of AI technologies with carbon reduction potential.

show abstract

G20 roadmap for carbon neutrality: The role of Paris agreement, artificial intelligence, and energy transition in changing geopolitical landscape

Salman,

Wang,

Qin

et al. 2024

Journal of Environmental Management

View full text Add to dashboard Cite

Can artificial intelligence achieve carbon neutrality? Evidence from a quasi-natural experiment

Cited by 6 publications

References 67 publications

Artificial Intelligence and Carbon Emissions in Manufacturing Firms: The Moderating Role of Green Innovation

Artificial Intelligence and Carbon Emissions in Manufacturing Firms: The Moderating Role of Green Innovation

Investigating the nonlinear carbon reduction effect of AI: empirical insights from China’s provincial level

G20 roadmap for carbon neutrality: The role of Paris agreement, artificial intelligence, and energy transition in changing geopolitical landscape

Contact Info

Product

Resources

About