The development of computing infrastructure has brought about increased productivity, but it has also brought about energy consumption and carbon emissions. Based on the panel data and business enterprise registration data of 279 prefecture-level cities from 2008 to 2021, using the econometric model system, this study investigates the relationship between computing infrastructure and carbon emission intensity, yielding several findings: First, our result finds that there is an inverse "U-shaped" pattern in the association between carbon emission intensity and computing infrastructure. According to several robustness tests, such as using IV method and PCSD model, the research conclusion still holds. Second, heterogeneity analysis indicates that our findings are particularly pronounced in central regions, hub cities and moderately digitally developed cities. Third, mechanism analysis shows that carbon emission intensity is influenced by computing infrastructure through energy consumption, green technological innovation, and servitization of the economic structure, with energy consumption also following an inverted "U-shaped" pattern. These findings contribute to understanding the environmental impact of digital infrastructure and offer insights for promoting sustainable development.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11608359 | PMC |
| http://dx.doi.org/10.1038/s41598-024-81677-4 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Physical unclonable in-memory computing for simultaneous protecting private data and deep learning models.
Nat Commun
January 2025
Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, China.
Compute-in-memory based on resistive random-access memory has emerged as a promising technology for accelerating neural networks on edge devices. It can reduce frequent data transfers and improve energy efficiency. However, the nonvolatile nature of resistive memory raises concerns that stored weights can be easily extracted during computation.
View Article and Find Full Text PDFIntegrating Machine Learning for Predictive Maintenance on Resource-Constrained PLCs: A Feasibility Study.
Sensors (Basel)
January 2025
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy.
This study investigates the potential of deploying a neural network model on an advanced programmable logic controller (PLC), specifically the Finder Opta™, for real-time inference within the predictive maintenance framework. In the context of Industry 4.0, edge computing aims to process data directly on local devices rather than relying on a cloud infrastructure.
View Article and Find Full Text PDFLoRa Resource Allocation Algorithm for Higher Data Rates.
Sensors (Basel)
January 2025
Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A2, Canada.
LoRa modulation is a widely used technology known for its long-range transmission capabilities, making it ideal for applications with low data rate requirements, such as IoT-enabled sensor networks. However, its inherent low data rate poses a challenge for applications that require higher throughput, such as video surveillance and disaster monitoring, where large image files must be transmitted over long distances in areas with limited communication infrastructure. In this paper, we introduce the LoRa Resource Allocation (LRA) algorithm, designed to address these limitations by enabling parallel transmissions, thereby reducing the total transmission time () and increasing the bit rate (BR).
View Article and Find Full Text PDFInductive Power Transfer Coil Misalignment Perception and Correction for Wirelessly Recharging Underground Sensors.
Sensors (Basel)
January 2025
Department of Mechanical Engineering, University of Utah, Salt Lake City, UT 84112, USA.
Field implementations of fully underground sensor networks face many practical challenges that have limited their overall adoption. Power management is a commonly cited issue, as operators are required to either repeatedly excavate batteries for recharging or develop complex underground power infrastructures. Prior works have proposed wireless inductive power transfer (IPT) as a potential solution to these power management issues, but misalignment is a persistent issue in IPT systems, particularly in applications involving moving vehicles or obscured (e.
View Article and Find Full Text PDFParameterization of the Differences in Neural Oscillations Recorded by Wearable Magnetoencephalography for Chinese Semantic Cognition.
Biology (Basel)
January 2025
School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
Neural oscillations observed during semantic processing embody the function of brain language processing. Precise parameterization of the differences in these oscillations across various semantics from a time-frequency perspective is pivotal for elucidating the mechanisms of brain language processing. The superlet transform and cluster depth test were used to compute the time-frequency representation of oscillatory difference (ODTFR) between neural activities recorded by optically pumped magnetometer-based magnetoencephalography (OPM-MEG) during processing congruent and incongruent Chinese semantics.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!