Research on Carbon Emission Efficiency
and Its Improvement Path of China’s Provinces
Based on SBM-ML Model and fsQCA Model

Liu, Liang; Jin, Ronghua; Liu, Shu; Yang, Yirui; Gong, Xiujuan; Li, Mengyue; Pan, Jun–Yi

doi:10.15244/pjoes/156688

Cited by 1 publication

(1 citation statement)

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“…Liu Chao [38] and others employed the LSTM model to forecast carbon emissions and successfully realized the task of predicting carbon emissions and achieved remarkable results, which provided strong support for research and application in this field. Chun-Sen Liu and Jian-Sheng Qu [39] used LSTM for carbon emission forecasting in the transport industry, and the outcomes demonstrated that LSTM outperforms the BP neural network and SVR Machine, which demonstrates the exceptional performance of LSTM in the domain of carbon emission forecasting.…”

Section: Prediction Methods Based On Recurrent Neural Networkmentioning

confidence: 99%

Application of Neural Networks on Carbon Emission Prediction: A Systematic Review and Comparison

Feng,

Chen,

et al. 2024

Energies

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The greenhouse effect formed by the massive emission of carbon dioxide has caused serious harm to the Earth’s environment, in which the power sector constitutes one of the primary contributors to global greenhouse gas emissions. Reducing carbon emissions from electricity plays a pivotal role in minimizing greenhouse gas emissions and mitigating the ecological, economic, and social impacts of climate change, while carbon emission prediction provides a valuable point of reference for the formulation of policies to reduce carbon emissions from electricity. The article provides a detailed review of research results on deep learning-based carbon emission prediction. Firstly, the main neural networks applied in the domain of carbon emission forecasting at home and abroad, as well as the models combining other methods and neural networks, are introduced, and the main roles of different methods, when combined with neural networks, are discussed. Secondly, neural networks were used to predict electricity carbon emissions, and the performance of different models on carbon emissions was compared. Finally, the application of neural networks in the realm of the prediction of carbon emissions is summarized, and future research directions are discussed. The article provides a reference for researchers to understand the research dynamics and development trend of deep learning in the realm of electricity carbon emission forecasting.

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Section: Prediction Methods Based On Recurrent Neural Networkmentioning

confidence: 99%