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

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Abstract

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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Funding

This work was supported by the National Natural Science Foundation of China (NSFC) [Grant Numbers 71771067, 71390522, 71671053]; the National Key R&D Program of China [Grant Number 2016YFC0701800]; and the Tsinghua University Energy Internet Research Institute (EIRI) Seed Fund Program.

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Authors and Affiliations

  1. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou, 510006, China

    Luqi Wang

  2. School of Management, Guangzhou University, Guangzhou, 510006, China

    Xiaolong Xue

  3. School of Management, Harbin Institute of Technology, Harbin, 150001, China

    Zebin Zhao

  4. Department of Civil and Environmental Engineering, University of Washington, Seattle, 98195, USA

    Yinhai Wang

  5. Business School, Sichuan University, Chengdu, 610065, China

    Ziqiang Zeng

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  1. Luqi Wang
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Correspondence to Zebin Zhao.

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Table 3 Descriptive statistics of the variables
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Wang, L., Xue, X., Zhao, Z. et al. Finding the de-carbonization potentials in the transport sector: application of scenario analysis with a hybrid prediction model. Environ Sci Pollut Res 27, 21762–21776 (2020). https://doi.org/10.1007/s11356-020-08627-1

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