Study on the economic benefits of carbon-neutral digital platforms for sustainable development based on the GPT-QRCNN model

Abstract

Introduction: This article proposes a method for assessing the economic benefits of carbon-neutral digital platforms, which promote sustainable development by reducing carbon emissions through digital technology and data platforms. Methods: The proposed method combines the GPT (Generative Pre-trained Transformer) and QRCNN (Quantile Regression Convolutional Neural Network) models. Firstly, the GPT model is utilized to extract features from platform data. Then, these features are combined with the QRCNN model for sequence modeling, enhancing prediction accuracy and generalization ability. Results: The method's effectiveness is demonstrated through experimental verification using actual platform data. The results highlight the practical significance and application value of the proposed method in evaluating the economic benefits of carbon-neutral digital platforms. Discussion: By leveraging digital technology and data platforms, carbon-neutral digital platforms aim to reduce carbon emissions and promote sustainable development. The proposed method provides a means to accurately predict and analyze the economic benefits associated with these platforms. The combination of the GPT and QRCNN models enhances the accuracy and generalization ability of economic benefit predictions, enabling informed decision-making and policy formulation.