The influence of regional tourism economy development on carbon neutrality for environmental protection using improved recurrent neural network

Abstract

Introduction: The escalation of the global economy has contributed to the emergence of several environmental challenges, such as global warming and the gradual depletion of the natural environment, which has adversely impacted people’s lives. In response, nations across the globe have embraced the carbon neutrality concept as a means to safeguard the environment and foster a green economy. Methods: This study assesses the environmental impact of the tourism economy concerning carbon neutrality. Firstly, the quantification of carbon emission-related data in the region is executed using a hierarchical analysis method to pre-process the data for model training. Secondly, this paper utilizes the LTC-RNN (liquid time constant-recurrent neural network) model for model training. The model training is based on expert evaluation labels and cross-validation to execute comparison experiments. Results: The evaluation results of the model with different training features are compared with the expert results, and the optimal model with 10 features is identified, achieving an accuracy of more than 85%. Finally, practical testing is conducted, and the outcomes indicate that the proposed method can accomplish the task efficiently. Discussion: The proposed method provides technical support for the environmental evaluation of the green tourism economy in the context of carbon neutrality. It also presents novel ideas for accelerating the carbon neutrality agenda and fostering a low-carbon economy.