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

Updata:01-01-1970

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By:Zhida Guo, Jingyuan Fu

DOI: https://doi.org/10.30564/ese.v2i1.1772

Abstract:
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 car
bon dioxide emissions using the improved method, the increments of data
were taken into account. The target values were obtained after the calcula
tion 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.
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