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Dynamic spatial spillover effect of new energy vehicle industry policies on carbon emission of transportation sector in China

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  • Zhao, Min
  • Sun, Tao

Abstract

In order to reduce vehicle carbon emission, China launched many policies to develop new energy vehicles (NEVs). Using the panel data of 30 provinces in China from 2009 to 2018, this paper constructs a dynamic spatial panel data model based on spatial Dubin model (SDM) to empirically analyze the dynamic spatial spillover effect of NEV industry policies on the carbon emissions of China's transportation sector. The results are as follows: Firstly, low carbon emissions of transportation sector in one region can promote the realization of low carbon emissions in spatially adjacent or economically similar regions, and this relationship shows a significant dynamic inertia and increasing trend. Secondly, the NEV industry policies have both short-term and long-term effects on carbon emissions of transportation sector in local region. Thirdly, NEV industry policies in one region can produce positive spatial spillover effect on carbon emissions of transportation sector in spatially adjacent or economically similar regions, but this effect needs long-term transmission to show significant effect. Fourthly, both the education level of regional residents and the investment scale of transportation sector are negatively correlated with the carbon emission of transportation sector. Based on the results, some suggestions are put forward.

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  • Zhao, Min & Sun, Tao, 2022. "Dynamic spatial spillover effect of new energy vehicle industry policies on carbon emission of transportation sector in China," Energy Policy, Elsevier, vol. 165(C).
    • Handle: RePEc:eee:enepol:v:165:y:2022:i:c:s0301421522002166
    DOI: 10.1016/j.enpol.2022.112991
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    Cited by:

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    2. Limei Ma & Qianying Wang & Dan Shi & Qinglong Shao, 2023. "Spatiotemporal patterns and determinants of renewable energy innovation: Evidence from a province-level analysis in China," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    3. Yali Hou & Qunwei Wang & Tao Tan, 2022. "Prediction of Carbon Dioxide Emissions in China Using Shallow Learning with Cross Validation," Energies, MDPI, vol. 15(22), pages 1-19, November.
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    6. Miaomiao Tao & Lim Thye Goh, 2023. "The Road to Improve Energy Efficiency vs. the Role of Corruption - A Dynamic Quantile Exploration," Asian Economics Letters, Asia-Pacific Applied Economics Association, vol. 4(1), pages 1-5.

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