The emerging driving force of inclusive green growth: Does digital economy agglomeration work?

Ren, Shuming; Li, Lianqing; Han, Yueqi; Hao, Yu; Wu, Haitao

doi:10.1002/bse.2975

Cited by 215 publications

(104 citation statements)

References 110 publications

(168 reference statements)

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“…In the short term (1-2 years), the contribution of computers to total factor productivity is almost zero. In the long term (more than 5 years), the contribution of computer investment to total factor productivity is more than five times that of investment [15]. Sánchez suggested that, in the improvement of digital economy, digital economy is a brand-new way to promote the circulation of commodities and the improvement of service industry.…”

Section: Related Workmentioning

confidence: 99%

SURF Algorithm-Based Data Aggregation Method and Digital Sharing Economy

Chen

Zhang

Hyng

et al. 2022

Mobile Information Systems

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Data aggregation technique removes redundant message in original data, reduces the amount of data transmission, reduces network communication overhead, improves message acquisition accuracy and collection efficiency, and prolongs the effective working time of the network. It is an energy-saving technique widely used in wireless sensor networks, one of the techniques. However, with the continuous operation in the wireless sensor network, the data aggregation technique saves the energy cost of the network, but also brings new security risks to the network. Therefore, how to ensure the confidentiality and integrity of data in the process of aggregation, calculation, storage, and forwarding is the core link of wireless sensor network security research. This paper uses SURF algorithm to study this question. The algorithm in this paper not only has good robustness to the data, but also introduces data aggregation and box filter in the calculation, so that the registration time can be better improved. After the research in this paper, the accuracy of the algorithm in this paper is 19% better than the traditional algorithm. At the same time, the accuracy of the aggregated data increases over time as the channel congestion decreases in the traditional process.

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Section: Related Workmentioning

confidence: 99%

SURF Algorithm-Based Data Aggregation Method and Digital Sharing Economy

Chen

Zhang

Hyng

et al. 2022

Mobile Information Systems

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show abstract

“…As for the influencing factors of the digital economy, the literature covers a wide range, such as digital infrastructure [ 21 ], digital application [ 21 ], education [ 22 ], science and technology [ 23 ], etc. From the existing literature, there are great differences in the development levels of infrastructure, education, technology, and so on among Chinese cities, so there are great differences in the development of the digital economy.…”

Section: Introductionmentioning

confidence: 99%

Has the Digital Economy Reduced Carbon Emissions?: Analysis Based on Panel Data of 278 Cities in China

Zhou

Liu

Zhu

2022

IJERPH

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China is undergoing an urbanization process at an unprecedented scale, and low-carbon urban development is of great significance to the completion of the “dual carbon goals”. At the same time, the digital economy has become an important engine for urban development, and its role in environmental improvement has become increasingly prominent. While the digital economy is booming, can it promote the low-carbon development of cities? Based on the panel data of 278 cities in China from 2011 to 2019, this paper discusses the impact of the digital economy on carbon emissions and the long-term development trend between the digital economy and carbon emissions, the impact of differences in the development level of the digital economy on carbon emissions reduction, and the impact of green energy efficiency in the relationship between the digital economy and carbon emissions. The results show that the digital economy has a significant inhibitory effect on carbon emissions, and with the development of the digital economy, more and more cities show an absolute decoupling of the digital economy and carbon emissions and are turning to low-carbon development. The development level of the digital economy has a heterogeneous impact on carbon emissions. With the improvement of the development level of the digital economy, the effect on emission reduction is more significant. As a threshold variable, green energy efficiency affects the relationship between digital economy and carbon emissions. When green energy efficiency is low, the digital economy promotes carbon emissions, and when green energy efficiency is high, the digital economy reduces carbon emissions.

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“…From the perspective of urban green development, the agglomeration of the digital economy can not only alleviate regional energy consumption and environmental pollution but also optimize the industrial structure and human capital, thereby promoting the green development of the regional environment (Ren et al, 2022). In addition, the digital economy can significantly improve the regional green total factor energy efficiency by improving the level of economic growth, urbanization, and research and development (R&D) .…”

Section: Impact Of Digital Economy On Urban Human Settlement Environmentmentioning

confidence: 99%

Theoretical framework and research prospect of the impact of China’s digital economic development on population

2022

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Against the background of social digitalization, networking, and intelligent development, the relationship between China’s digital economy and regional factors is increasingly close. This study systematically cards the relevant references of the digital economy, regional factors, and population based on the six perspectives of industry, residents’ income, urban human settlement environment, infrastructure, the real economy, and government management level to explore the influence of the digital economy on regional factors and regional factors on population. The results show that: 1) the digital economy has a bidirectional influence on the population. Digital economy can both attract population and lead to population outflow through relevant regional factors. 2) Digital economy can indirectly affect the spatial layout of population attributes by giving digital connotations to regional elements. We analyze the influence of China’s digital economic development on the population, build the influence of the digital economy on the population research framework, and put forward the research prospect of the impact of the digital economy on the population to provide a new research perspective on digital economic geography and population, as well as provide significant reference to guide the reasonable population flow and narrow the digital divide gap.

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The emerging driving force of inclusive green growth: Does digital economy agglomeration work?

Cited by 215 publications

References 110 publications

SURF Algorithm-Based Data Aggregation Method and Digital Sharing Economy

SURF Algorithm-Based Data Aggregation Method and Digital Sharing Economy

Has the Digital Economy Reduced Carbon Emissions?: Analysis Based on Panel Data of 278 Cities in China

Theoretical framework and research prospect of the impact of China’s digital economic development on population

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