Data‐driven models employed to waste plastic in China: Generation, classification, and environmental assessment

It is crucial to precisely predict the generation of plastic waste and realize its fine classification in terms of policy demand and environmental benefits. Only a few studies have used the date‐driven model to forecast the amount of plastic waste. The benefits of classifying the plastic waste using deep learning have also been only scarcely reported in the literature. Therefore, this study used the Prophet model to estimate the amount of plastic waste from 2005 to 2025. Four types of Visual Geometry Group Networks based on Transfer Learning (TLVGGNet) were performed for classifying the plastic waste. Potentials of saved energy, the reduction of green‐house gases emission (GHG), and air pollutants were also discussed under different scenarios (current recycling system and TLVGGNet system). The results showed that the amount of waste plastic was anticipated to be 26.44 Mt in 2020 and 33.18 Mt in 2025 in China. The method of transfer learning could shorten the training time and improve the performance of the TLVGGNet‐11 model in the test dataset (41.6–68.1%). Moreover, TLVGGNet‐16 was considered to be the most optimum model for plastic waste classification in terms of training time (83.94 s), accuracy (75.5%), precision (76.9%), recall (75.5%), and F1 score (75.1%). The TLVGGNet‐16 system contributes about 12.15–15.97% in terms of electricity‐savings. Compared with the current recycling system, the amount of CO2 emissions saved and reduction in CH4 emissions could be more than 8–10% and 0.4–0.5%, respectively, in the TLVGGNet‐16 system. The saved VOCS and NOX emissions were within the ranges of 34.84–127.22 billion kg and 93.64–414.14 billion kg between 2017 and 2025 using the method of deep learning.