An Approach to Carbon Emissions Prediction Using Generalized Regression Neural Network Improved by Genetic Algorithm


  • Zhida Guo School of Economics and Management, Dalian Jiaotong University, Dalian, China
  • Jingyuan Fu Academic Unit of Human Communication, Development, and Information Sciences, Faculty of education, the University of Hong Kong, Hong Kong, China



The study on scientific analysis and prediction of China’s future carbonemissions is conducive to balancing the relationship between economicdevelopment and carbon emissions in the new era, and actively respondingto climate change policy. Through the analysis of the application of thegeneralized regression neural network (GRNN) in prediction, this paperimproved the prediction method of GRNN. Genetic algorithm (GA) wasadopted to search the optimal smooth factor as the only factor of GRNN,which was then used for prediction in GRNN. During the prediction of carbon dioxide emissions using the improved method, the increments of datawere taken into account. The target values were obtained after the calculation of the predicted results. Finally, compared with the results of GRNN,the improved method realized higher prediction accuracy. It thus offers anew way of predicting total carbon dioxide emissions, and the predictionresults can provide macroscopic guidance and decision-making referencefor China’s environmental protection and trading of carbon emissions.


Carbon emissions, Genetic Algorithm, Generalized Regression Neural Network, Smooth Factor, Prediction


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How to Cite

Guo, Z., & Fu, J. (2020). An Approach to Carbon Emissions Prediction Using Generalized Regression Neural Network Improved by Genetic Algorithm. Electrical Science & Engineering, 2(1), 4–10.


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