Does the Digital Economy Successfully Facilitate Carbon Emission Reduction in China? Green Technology Innovation Perspective

Hou, Jian; Bai, Wanting; Sha, Dexuan

doi:10.1177/09717218231161235

Cited by 14 publications

(5 citation statements)

References 41 publications

(55 reference statements)

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“…By referring to relevant studies 49 – 51 and combining with the actual development of the 19th Urban agglomeration, this paper comprehensively considers the principles of scientificity, systematicness, robustness and data availability of the indexes to construct the TG system evaluation index system of urban agglomeration. (See Table 4 ).…”

Section: Methodsmentioning

confidence: 99%

Research on the coupling mechanism and influencing factors of digital economy and green technology innovation in Chinese urban agglomerations

Zhong,

Duan,

Liu

et al. 2024

Sci Rep

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This paper examines the coupling coordination degree between digital economy and green technology innovation in 19 urban agglomerations across China from 2011 to 2020. Through the analysis of the coupling coordination degree model, spatial autocorrelation, multi-distance spatial clustering analysis, kernel density analysis and grey correlation model, this study uncovers the mechanism of coupling between digital economy and green technology in Chinese urban agglomerations. Data analysis revealed a significant increase in the coupling coordination between the digital economy and green technology innovation within urban agglomerations. However, there are noticeable spatial imbalances in this trend. Additionally, the multi-distance spatial distance analysis highlights a shift from a random distribution to a clustered distribution of spatial characteristics. The polarization features vary among each urban agglomeration and exhibit a significant positive spatial correlation. Factors such as economic sustainability, creative talent, policy support, digital impetus, and technological support will affect the coupling mechanism of green technology innovation and the digital economy in China's urban agglomerations. Policy recommendations are proposed to foster the development of the digital economy, promote coordinated growth within and beyond urban clusters, and ultimately build a digital ecological civilization that is both green and intelligent.

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Section: Methodsmentioning

confidence: 99%

Research on the coupling mechanism and influencing factors of digital economy and green technology innovation in Chinese urban agglomerations

Zhong,

Duan,

Liu

et al. 2024

Sci Rep

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“…Considering the consistency of the agricultural industrial structure, statistical items, and data comparability in various regions of China, this paper uses the emission coefficient method to measure agricultural carbon emissions according to the 2006 National Greenhouse Gas Inventory Guidelines issued by the United Nations Intergovernmental Panel on Climate Change (IPCC). Referring to the practices of Hou et al (2023), Shen et al (2023), andTian et al (2023), this paper divides agricultural carbon emission sources into four dimensions, namely, agricultural materials, soil, paddy fields, and livestock breeding. The specific calculation formula is as follows:…”

Section: Calculation Of Agricultural Carbon Emissions-the Emission Co...mentioning

confidence: 99%

Prediction of agricultural carbon emissions in China based on a GA-ELM model

Guo,

Yang,

Shen

et al. 2023

Front. Energy Res.

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Introduction: Strengthening the early warning of greenhouse gas emissions from agriculture is an important way to achieve Goal 13 of the Sustainable Development Goals. Agricultural carbon emissions are an important part of greenhouse gases, and accelerating the development of green and low-carbon agriculture is of great significance for China to achieve high-quality economic development and the goal of “carbon neutrality in peak carbon dioxide emissions”.Methods: By measuring the total agricultural carbon emissions in China and seven administrative regions from 2000 to 2021, the paper analyzes the influencing factors of agricultural carbon emissions in China by using STIRPAT environmental pressure model, and on this basis, predicts the peak trend of agricultural carbon emissions in China under different development scenarios by using the extreme learning machine model optimized by genetic algorithm.Results: The results showed that the extreme learning machine model improved by the genetic algorithm can overcome the shortcoming that the extreme learning machine model is easy to fall into the local optimal solution, thus obtaining higher prediction accuracy. At the same time, from 2000 to 2021, the total agricultural carbon emissions in China showed a continuous fluctuation trend, and due to the constraints of the agricultural economic level, agricultural industrial structure, and agricultural human capital, the agricultural carbon emissions showed spatial differentiation. It is worth noting that, in the context of green development, the agricultural carbon emissions of the seven regions in China all have the potential to achieve the “peak carbon dioxide emissions” goal in 2030, with only a slight difference at the peak.Discussion: The research results of this paper provide evidence for the government to formulate flexible, accurate, reasonable and appropriate agricultural carbon reduction policies, which is helpful to strengthen the exchanges and cooperation of regional agricultural and rural carbon reduction and fixation, and actively and steadily promote China's agriculture to achieve the goal of “peak carbon dioxide emissions carbon neutrality”.

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“…The authors of the study (Jian Hou et al, 2023) evaluate China's digital economy based on the experience of regional data and build a nonlinear dynamic threshold model to incorporate the heterogeneous threshold of green technology innovation into the influencing mechanism. The authors investigated that green technology innovation has a heterogeneous threshold effect between the digital economy and carbon emissions: a lower level of green technology innovation contributes to the carbon impact of the digital economy to some extent, but when green technology innovation increases and exceeds the threshold, the digital economy can dramatically reduce regional emissions carbon.…”

Section: Literature Reviewmentioning

confidence: 99%

The use of Digital Technologies to Ensure Environmental Safety in the Context of the Green Economy Development

Arefiev,

Zhyhlei,

Pereguda

et al. 2023

RDLUZ

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The purpose of the article is to justify the use of digital technologies to ensure environmental safety in the context of the green economy development, taking into account its tasks and principles. The research methodology is a comprehensive approach that makes it possible to comprehensively approach the methods and techniques of scientific knowledge, to carry out a comprehensive analysis of the subject of research, to reveal the essence of the green economy and the purpose of implementing digital technologies to ensure environmental safety, to follow the system-forming connections between economic, social, and ecological processes in a global dimension, to highlight the directions of using digital technologies to ensure environmental safety, which gives a synergistic effect for the development of the green economy. It has been proven that the use of artificial intelligence technologies, information logistics systems make it possible to solve many problems in agriculture in the process of production, processing, transportation and preservation of agricultural products. Highlighting the negative consequences of digitization in relation to the environmental component, which includes the increase in energy consumption, electronic waste and cyber security, made it possible to outline directions for their leveling.

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Does the Digital Economy Successfully Facilitate Carbon Emission Reduction in China? Green Technology Innovation Perspective

Cited by 14 publications

References 41 publications

Research on the coupling mechanism and influencing factors of digital economy and green technology innovation in Chinese urban agglomerations

Research on the coupling mechanism and influencing factors of digital economy and green technology innovation in Chinese urban agglomerations

Prediction of agricultural carbon emissions in China based on a GA-ELM model

The use of Digital Technologies to Ensure Environmental Safety in the Context of the Green Economy Development

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