How does digital economy affect green total factor productivity? Evidence from China

Lyu, Yanwei; Wang, Wenqiang; Wu, You; Zhang, Jinning

doi:10.1016/j.scitotenv.2022.159428

Cited by 205 publications

(101 citation statements)

References 82 publications

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“…Figure 1 reports the U-shaped relationship between GTFP and AI. This is in agreement with the conclusions reached by Zhao et al (2022) [ 18 ] and Lyu et al (2022) [ 7 ]. However, the difference with the study of Zhao et al (2022) lies in the choice of econometric model; the coefficients of AI and its quadratic term in the fixed effects model in the study of Zhao et al (2022) are not significant but significant in the GMM model.…”

Section: Resultssupporting

confidence: 94%

“…Lyu et al (2022) conducted a mechanism test on the digital economy and GTFP and found that the impact of the digital economy on GTFP is mainly realized through factor market distortion and industrial structure upgrading [ 7 ]. In this paper, we choose another perspective of mechanism testing, i.e., we try to explore the moderating effect of environmental decentralization mechanism.…”

Section: Resultsmentioning

confidence: 99%

“…The study by Zhang et al (2022) confirmed the U-shaped effect of AI on GTFP using a nonlinear dynamic panel regression model [ 16 ], which discussed the effect of regional resource heterogeneity but did not conduct an analysis of spatial spillover effects between regions. Lyu et al (2022) confirmed the U-shaped relationship between both digital economy and GTFP, using a comprehensive indicator built from four dimensions of digital economy infrastructure, digital talent, digital application, and digital economy development environment to measure the digital economy [ 7 ]. In this paper, we refine the research object and select only one of the digital economy infrastructure indicators to explore whether AI will have a different impact on GTFP.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The Ministry of Science and Technology 2022 issued a “notice on supporting the construction of a new generation of artificial intelligence demonstration application scenarios” that requires giving full play to the role of artificial intelligence to empower economic and social development, and building a whole chain and process of artificial intelligence industry application ecology. The application of artificial intelligence in the chain makes it easier to diminish the overall influence over the environment by eliminating the need for excessive use of resources [ 6 ], and artificial intelligence drives the evolution of the economy from labor- and capital-intensive to technology-intensive [ 7 ], changing the way society and the economy, as a whole, operate [ 8 ]. Artificial intelligence can extract the value of production data, identify energy consumption bottlenecks, reduce production costs, and refine clean production management [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence can extract the value of production data, identify energy consumption bottlenecks, reduce production costs, and refine clean production management [ 9 ]. The greatest advantage of exploring the link between AI and GTFP is the combined impact of economic growth, energy efficiency, and the environment that can be derived [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Impact of Artificial Intelligence on Regional Green Development under China’s Environmental Decentralization System—Based on Spatial Durbin Model and Threshold Effect

Fang

Cao

Sun

2022

IJERPH

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Artificial intelligence (AI) is the core technology of digital economy, which leads the transition to a sustainable economic growth approach under the Chinese-style environmentally decentralized system. In this paper, we first measured the green total factor productivity (GTFP) of 30 Chinese provinces from 2011 to 2020 using the super-efficiency slacks-based measure (SBM) model, analyzed the mechanism of the effect of AI on GTFP under the environmental decentralization regime, and secondly, empirically investigated the spatial evolution characteristics and the constraining effect of the impact of AI on GTFP using the spatial Durbin model (SDM) and the threshold regression model. The findings reveal: a U shape of the correlation of AI with GTFP; environmental decentralization acts as a positive moderator linking AI and GTFP; the Moran index demonstrates the spatial correlation of GTFP; under the constraint of technological innovation and regional absorptive capacity as threshold variables, the effect of AI over GTFP is U-shaped. This paper provides a useful reference for China to accelerate the formation of a digital-driven green economy development model.

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Section: Resultssupporting

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Impact of Artificial Intelligence on Regional Green Development under China’s Environmental Decentralization System—Based on Spatial Durbin Model and Threshold Effect

Fang

Cao

Sun

2022

IJERPH

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Carbon emission efficiency in the age of digital economy: New insights on green technology progress and industrial structure distortion

Yu,

Liu,

Yan

et al. 2024

Bus Strat Env

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There is a dual impact of the digital economy (DE) on carbon emission efficiency (CE). An in‐depth study of the impact of the DE on “local‐adjacent” CE at different spatial scales can support the debate on whether the digital economy is empowerment or impairment. This study reassesses the DE at the urban level from a micro‐perspective by cross‐referencing the data of listed companies with the information obtained from city yearbooks. Considering the potential spatial correlation of the digital economy, we use the spatial Durbin model to examine 281 cities from 2011 to 2019 and study the influence of the DE on “local‐adjacent” CE. We further explain the pathways of this influence from the perspectives of green technological progress and industrial structural distortion. The study findings indicate that the impact of DE on local CE exhibits a “U”‐shaped pattern overall while its impact on neighboring CE shows an inverted “U”‐shaped pattern. These conclusions are still valid even after conducting a series of robustness tests, including spatial difference‐in‐differences and spatial replacement matrix. Further research reveals that the impact of DE on neighboring CE weakens with increasing spatial distance. The promoting effect of DE on neighboring CE peaks at 250 km, and the inverted “U”‐shaped pattern holds within a range of 300 km. In the initial stages, green technological progress can mask the negative impact of DE on local CE while simultaneously improving CE within a 250‐km range of neighboring areas. However, in the later stages, DE exacerbates the distortion of neighboring industrial structures, thereby reducing neighboring CE. The lack of synergy in the scale and growth rate of DE development in different regions significantly reduces the enhancement effect of the digital economy on CE.

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Green finance policy and enterprise green development: Evidence from China

Lyu

Bai

Zhang

2023

Corp Soc Responsibility Env

Self Cite

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Global environmental issues are becoming increasingly prominent, and green finance may play a key role in achieving sustainable development. This study takes the “Green Financial Reform and Innovation Pilot Zone” policy as a quasi‐natural experiment. The Difference‐in‐Difference model and Propensity Score Matching‐Difference in Difference model are applied to examine the direct effect of green finance policy on the green development of enterprises. A mechanism test model is used to investigate its indirect transmission mechanism. Then several heterogeneity analyses are performed from five perspectives. The results show that green finance policy considerably promotes enterprise green development and the conclusion is supported by a series of robustness tests. The mechanism analyses show that financing constraints and green innovation are the two transmission channels for green finance policy affecting enterprise green development. The heterogeneity analyses indicate that the policy effect is more significant among enterprises those are non‐state‐owned, have managers with a financial background, operate in regions with high marketization, belong to industries with low monopoly, and locate in poilt zones of Zhejiang, Guangdong, and Guizhou. Based on the above findings, policy suggestions are made to establish a win‐win scenario for the economy and the environment.

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How does digital economy affect green total factor productivity? Evidence from China

Cited by 205 publications

References 82 publications

Impact of Artificial Intelligence on Regional Green Development under China’s Environmental Decentralization System—Based on Spatial Durbin Model and Threshold Effect

Impact of Artificial Intelligence on Regional Green Development under China’s Environmental Decentralization System—Based on Spatial Durbin Model and Threshold Effect

Carbon emission efficiency in the age of digital economy: New insights on green technology progress and industrial structure distortion

Green finance policy and enterprise green development: Evidence from China

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