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

Abstract

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.

Quantitative evaluation of science and technology financial policies based on the PMC-AE index model: A case study of China’s science and technology financial policies since the 13th five-year plan

“…Shen et al [ 37 ] used the difference-in-differences approach to empirically analyze the policy effects of the energy rights trading system, and applied the Analytic Hierarchy Process (AHP) to evaluate China’s military-civil fusion policies [ 38 ]. Feng et al [ 39 ] constructed a BP neural network-based model for predicting carbon emissions in the electricity sector; some scholars used the Delphi method and Fuzzy Analytic Hierarchy Process (FAHP) to assess the main factors and sub-factors of green finance [ 40 ]; analyzed the risks related to China’s green infrastructure financing and prioritized them [ 41 ], as well as evaluating the environmental, social, and governance (ESG) factors and policy choices in China’s green finance investment decisions [ 42 ]. Currently, many evaluation methods have certain flaws, mainly reflected in the inevitable subjectivity and lower precision.…”