An approach to carbon emissions prediction using generalized regression neural network improved by genetic algorithm

Zhida Guo (Associate professor, PhD, School of Economics and Management, Dalian Jiaotong University, Dalian, China)
Jingyuan Fu (PhD Candidate, 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 carbon emissions is conducive to balancing the relationship between economic development and carbon emissions in the new era, and actively responding to climate change policy. Through the analysis of the application of the generalized regression neural network (GRNN) in prediction, this paper improved the prediction method of GRNN. Genetic algorithm (GA) was adopted 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 data were 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 a new way of predicting total carbon dioxide emissions, and the prediction results can provide macroscopic guidance and decision-making reference for China’s environmental protection and trading of carbon emissions.


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

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