Blockchain and Machine Learning in the Green Economy: Pioneering Carbon Neutrality Through Innovative Trading Technologies

Abstract

In response to the pressing imperative of combating climate change on a global scale, a new era of carbon neutrality is steadily emerging. Achieving carbon neutrality is critical, and in the digital economy, technology-driven business models are essential for reducing carbon emissions through effective carbon emission trading systems. However, current research on carbon emission trading suffers from inadequate privacy protection, low efficiency in data sharing and model construction, as well as insufficient capabilities in automated and autonomous model building. Therefore, this study focuses on utilizing blockchain and automated machine learning for data sharing and modeling to enhance carbon neutrality. First, we design the architecture of the system and the mechanism for storing data on the blockchain. We then devise methods for storing and trading carbon emission transactions on the blockchain and construct the process for issuing carbon credits. In addition, our proposed method incorporates neural architecture search to develop a carbon trading price forecasting model. By leveraging data augmentation for carbon emission price time series and utilizing triplet loss for model training, we enhance the reliability and security of carbon trading investment through accurate price forecasting. The experimental results further demonstrate the robust performance and precision of our carbon emission price forecasting module. Consequently, our approach provides efficient carbon emission trading services to businesses and individuals, offering a robust solution for global carbon emission reduction and the achievement of carbon neutrality.