Novel economy and carbon emissions prediction model of different countries or regions in the world for energy optimization using improved residual neural network.

Nowadays, the world has achieved tremendous economic development at the expense of the long-term habitability of the planet. With the rapid economic development, the global greenhouse effect caused by excessive carbon dioxide (CO ₂ ) emissions is also accumulating, which generates the negative impact of global warming on nature and human beings. Meanwhile, economy and CO ₂ emissions prediction methods based on traditional neural networks lead to gradient disappearance or gradient explosion, making the economy and CO ₂ emissions prediction inaccurate. Therefore, this paper proposes a novel economy and CO ₂ emissions prediction model based on a residual neural network (RESNET) to optimize and analyze energy structures of different countries or regions in the world. The skip links are used in the inner residual block of the RESNET to alleviate vanishing gradients due to increasing depth in deep neural networks. Consequently, the proposed RESNET can optimize this problem and protect the integrity of information by directly bypassing the input information to the output, which can increase the precision of the prediction model. The needs for natural gas, hydroelectricity, oil, coal, nuclear energy, and renewable energy in 24 different countries or regions from 2009 to 2020 are used as inputs, the CO ₂ emissions and the gross domestic product (GDP) per capita are respectively used as the undesired output and the desired output of the RESNET to build an economy and CO ₂ emissions prediction model. The experimental results show that the RESNET has higher correctness and functionality than the traditional convolutional neural network (CNN), the radial basis function (RBF), the extreme learning machine (ELM) and the back propagation (BP). Furthermore, the proposed model provides guidance and development plans for countries or regions with low energy efficiency, which can improve energy efficiency, economic development and reasonably control CO ₂ emissions.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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