Finding the de-carbonization potentials in the transport sector: application of scenario analysis with a hybrid prediction model

De-carbonization of the transport sector is an important pathway to climate-change mitigation and presents the potential for future lower emissions. To assess the potential quantitatively under different optimization measures, this paper presents a hybrid model combining an integrated machine learning model with the scenario analysis. We compare the training accuracy of the back-propagation neural networks (BPNN), Gaussian process regression (GPR), and support vector machine (SVM) fitting model with different training datasets. The results indicate that the performance of the SVM model is superior to other methods. And the particle swarm optimization (PSO) algorithm is then used to optimize hyper-parameters of the SVM model. Two scenarios including business as usual (BAU) and best case (BC) are set according to the current trends and target trends of driving factors identified by the extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model. Finally, to find the de-carbonization potentials in the transport sector, the PSO-SVM model is applied to predict transport emissions from 2015 to 2030 under two scenarios. Results show that transport emissions reduce by about 131.36 million tons during 2015–2020 and 372.86 million tons during 2021–2025 in the BC scenario. The findings can effectively track, test, and predict the achievement of policy goals and provide practical guidance for de-carbonization development.

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