Abstract
IntroductionThe 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.MethodsThis 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.ResultsThe 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.DiscussionThe 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.
Subject
Ecology,Ecology, Evolution, Behavior and Systematics
Cited by
3 articles.
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1. LTC-AE: Liquid Time Constant Autoencoders for Time Series Anomaly Detection;2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS);2024-06-28
2. ISSA-enhanced GRU-Transformer: integrating sports wisdom into the frontier exploration of carbon emission prediction;Frontiers in Ecology and Evolution;2024-03-18
3. Deep Learning in Carbon Neutrality Forecasting;Journal of Organizational and End User Computing;2024-01-17