Carbon emission allowance price forecasting for China Guangdong carbon emission exchange via the neural network
Abstract
Carbon emission allowance price forecasting is a significant issue for policy makers and investors with the world transitioning to green energy and devoting enormous efforts to be more sustainable. This study explores usefulness of the nonlinear autoregressive neural network for this forecasting problem in a dataset of daily closing prices of carbon emission allowances traded in China Guangdong Carbon Emission Exchange during 19 December 2013–20 August 2021. Through examining various model settings across the algorithm, delay, hidden neuron, and data splitting ratio, the model leading to generally accurate and stable performance is reached. Usefulness of the machine learning technique for the price forecasting problem of the carbon emission allowance price is illustrated. Results here might be used on a standalone basis as technical forecasts or combined with fundamental forecasts to form perspectives of price trends and perform policy analysis, which could better assist different stakeholders in understanding energy cost and planning for green transition.
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