Can artificial intelligence improve green economic growth? Evidence from China

Yu, Qian; Liu, Jun; Shi, Lifan; Forrest, Jeffrey Yi‐Lin; Yang, Zhidan

doi:10.1007/s11356-022-23320-1

Cited by 35 publications

(13 citation statements)

References 88 publications

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“…Currently, artificial intelligence technology and industry are rapidly integrating and developing, leading to the expansion of industrial chain layout, the formation of industrial chain clusters, and the promotion of transformation and upgrading in traditional industries as well as high-quality economic development. Based on this, China has incorporated artificial intelligence into the national strategic level, and attaches great importance to intelligent technological progress and industrial development 11 , 12 .…”

Section: Introductionmentioning

confidence: 99%

The influence of AI on the economic growth of different regions in China

Lin,

Wang,

Jing

et al. 2024

Sci Rep

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High-quality development plays a crucial role in China’s economic progress in the new era. It represents a new concept of advancement and mirrors the increasing aspirations of the populace for an improved standard of living. In this context, the role of artificial intelligence (AI) in promoting sustainable development cannot be overemphasized. This paper explores how AI technologies can drive the transition to a green, low-carbon and circular economy. We have established an index system to measure the development level of the artificial intelligence industry and the high-quality development of the economy, which is relevant to the current state of the artificial intelligence industry and the advancement of the economy. Panel data from 2008 to 2017 has been utilized for this purpose. Global principal component analysis method and entropy value method are employed in the evaluation. Through in-depth analysis of the application of artificial intelligence and environmental protection in various provinces and cities, we clarify that artificial intelligence promotes innovation, saves resources, and is conducive to the development of green economy in the new era.

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Section: Introductionmentioning

confidence: 99%

The influence of AI on the economic growth of different regions in China

Lin,

Wang,

Jing

et al. 2024

Sci Rep

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“…Compared with regions with a lower level of economic development, regions with a higher degree of economic development have a more complete industrial system, and their industrial intelligence enhancement has a greater impact on the input and output of production factors [ 82 ]. Therefore, it is necessary to carry out a heterogeneity analysis from the perspective of the level of economic development.…”

Section: Heterogeneity Analysismentioning

confidence: 99%

Assessing the impact of industrial intelligence on urban carbon emission performance: Evidence from China

Tu,

Zang,

et al. 2024

Heliyon

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“…These models have demonstrated noteworthy advantages, featuring significantly reduced computation times and proving effective in addressing real-world challenges. Their applications span diverse fields, ranging from medicine (e.g., Rajpurkar et al, 2022) and economics (e.g., Qian et al, 2023) to water-related issues (e.g., Ray et al, 2023). Broadly speaking, one prevailing paradigm emphasizes the model-centric approach, placing a paramount focus on the model aspect of the equation.…”

Section: Model-centric Vs Data-centric Paradigmsmentioning

confidence: 99%

A call for a fundamental shift from model-centric to data-centric approaches in hydroinformatics

Zolghadr-Asli,

Ferdowsi,

Savić

2024

Camb. prisms Water

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Over the years, data-driven models have gained notable traction in water and environmental engineering. The adoption of these cutting-edge frameworks is still in progress in the grand scheme of things, yet for the most part, such attempts have been centered around the models themselves, and their internal computational architecture, that is, the model-centric approach. These endeavors can certainly pave the way for more tailor-fitted models capable of producing accurate results. However, such a perspective often neglects a fundamental assumption of these models, which is the importance of reliability, correctness, and accessibility of the data used in constructing them. This challenge arises from the prevalent model-centric paradigm of thinking in the field. An alternative approach, however, would prioritize placing data at the focal point, focusing on systematically enhancing current datasets and devising frameworks to improve data collection schemes. This suggests a paradigm shift toward more data-centric thinking in water and environmental engineering. Practically, this shift is not without challenges and necessitates smarter data collection rather than an excessive one. Equally important is the ethical and accurate collection of data, making it available to everyone while safeguarding the rights of individuals and other legal entities involved in the process.

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Can artificial intelligence improve green economic growth? Evidence from China

Cited by 35 publications

References 88 publications

The influence of AI on the economic growth of different regions in China

The influence of AI on the economic growth of different regions in China

Assessing the impact of industrial intelligence on urban carbon emission performance: Evidence from China

A call for a fundamental shift from model-centric to data-centric approaches in hydroinformatics

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