What does the digital economy bring to household carbon emissions? – From the perspective of energy intensity

Du, Zhili; Xu, Jie; Lin, Boqiang

doi:10.1016/j.apenergy.2024.123613

Applied Energy

2024

DOI: 10.1016/j.apenergy.2024.123613

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What does the digital economy bring to household carbon emissions? – From the perspective of energy intensity

Zhili Du,

Jie Xu,

Boqiang Lin

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2024

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Cited by 2 publications

References 67 publications

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How the digital economy is empowering green strategies for breaking carbon lock-in

Zhao,

Zhong,

Liu

et al. 2024

Journal of Environmental Management

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How the digital economy is empowering green strategies for breaking carbon lock-in

Zhao,

Zhong,

Liu

et al. 2024

Journal of Environmental Management

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Driver Analysis and Integrated Prediction of Carbon Emissions in China Using Machine Learning Models and Empirical Mode Decomposition

Suo,

Wang,

Han

2024

Mathematics

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Accurately predicting the trajectory of carbon emissions is vital for achieving a sustainable shift toward a green and low-carbon future. Hence, this paper created a novel model to examine the driver analysis and integrated prediction for Chinese carbon emission, a large carbon-emitting country. The logarithmic mean divisia index (LMDI) approach initially served to decompose the drivers of carbon emissions, analyzing the annual and staged contributions of these factors. Given the non-stationarity and non-linear characteristics in the data sequence of carbon emissions, a decomposition–integration prediction model was proposed. The model employed the empirical mode decomposition (EMD) model to decompose each set of data into a series of components. The various carbon emission components were anticipated using the long short-term memory (LSTM) model based on the deconstructed impacting factors. The aggregate of these predicted components constituted the overall forecast for carbon emissions. The result indicates that the EMD-LSTM model greatly decreased prediction errors over the other comparable models. This paper makes up for the gap in existing research by providing further analysis based on the LMDI method. Additionally, it innovatively incorporates the EMD method into the carbon emission study, and the proposed EMD-LSTM prediction model effectively addresses the volatility characteristics of carbon emissions and demonstrates excellent predictive performance in carbon emission prediction.

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What does the digital economy bring to household carbon emissions? – From the perspective of energy intensity

Cited by 2 publications

References 67 publications

How the digital economy is empowering green strategies for breaking carbon lock-in

How the digital economy is empowering green strategies for breaking carbon lock-in

Driver Analysis and Integrated Prediction of Carbon Emissions in China Using Machine Learning Models and Empirical Mode Decomposition

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