Decoding China’s Transport Decarbonization Pathways: An Interpretable Spatio-Temporal Neural Network Approach with Scenario-Driven Policy Implications

Yanming Sun (), Kaixin Liu and Qingli Li
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Yanming Sun: College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China
Kaixin Liu: College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China
Qingli Li: International Cooperation Center of National Development and Reform Commission, Beijing 100038, China

Sustainability, 2025, vol. 17, issue 15, 1-21

Abstract: The transportation sector, as a major source of carbon emissions, plays a crucial role in the realization of dual carbon goals worldwide. In this study, an improved least absolute shrinkage and selection operator (LASSO) is used to identify six key factors affecting transportation carbon emissions (TCEs) in China. Aiming at the spatio-temporal characteristics of transportation carbon emissions, a CNN-BiLSTM neural network model is constructed for the first time for prediction, and an improved whale optimization algorithm (EWOA) is introduced for hyperparameter optimization, finding that the prediction model combining spatio-temporal characteristics has a more significant prediction accuracy, and scenario forecasting was carried out using the prediction model. Research indicates that over the past three decades, TCEs have demonstrated a rapid growth trend. Under the baseline, green, low-carbon, and high-carbon scenarios, peak carbon emissions are expected in 2035, 2031, 2030, and 2040. The adoption of a low-carbon scenario represents the most advantageous pathway for the sustainable progression of China’s transportation sector. Consequently, it is imperative for China to accelerate the formulation and implementation of low-carbon policies, promote the application of clean energy and facilitate the green transformation of the transportation sector. These efforts will contribute to the early realization of dual-carbon goals with a positive impact on global sustainable development.

Keywords: LASSO; EWOA-CNN-BiLSTM; scenario prediction; transportation carbon emissions (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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