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The effect of low-carbon transportation pilot policy on carbon performance: evidence from China

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Abstract

In 2011, aiming to achieve sustainable development in the transportation sector, the Chinese government started a pilot policy of low-carbon transportation system (LCTS). Based on the panel data for 280 prefecture-level cities in China from 2006 to 2017, we first measure carbon efficiency by using the SBM-DEA model, and identify the direct and spatial spillover effects of LCTS on carbon efficiency and carbon intensity by adopting a spatial difference-in-differences approach (SDID). The results indicate that LCTS construction not only enhances local carbon performance but also has a significant spatial spillover effect in neighboring cities. The results are still valid after a series of robustness tests. The mechanism analysis reveals that LCTS can elevate carbon performance by improving energy efficiency, green innovation, and developing public transit. The direct and indirect effects of LCTS on carbon performance show more pronounced effects in megalopolis and eastern region. This paper provides reliable empirical evidence for the effect of LCTS on carbon performance, which is conducive to deepening the understanding of carbon emissions and has a high reference value for the rational formulation of carbon reduction policies.

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Notes

  1. The first batch of pilot cities includes 10 cities: Tianjin, Chongqing, Shenzhen, Xiamen, Hangzhou, Nanchang, Guiyang, Baoding, Wuxi, and Wuhan. The second batch of pilot cities include 16 cities: Beijing, Kunming, Xi'an, Ningbo, Guangzhou, Shenyang, Harbin, Huai'an, Yantai, Haikou, Chengdu, Qingdao, Zhuzhou, Bengbu, Shiyan, and Jiyuan. LCTS policy covers 26 cities in total. (Source: https://www.mot.gov.cn/).

  2. This paper uses the fourth version of DMSP annual data and the second version of VIIRS annual data released by NOAA to represent the level of urbanization. In order that the two sets of data can be compared. Firstly, the obtained DMSP annual data are corrected, then the VIIRS annual data are denoised, and the two sets of data coincidence years 2012 and 2013 are extracted for sensitivity analysis to select the optimal fitting parameters. Then, according to the selected optimal parameters, the annual data of VIIRS (2012–2018) is calculated into the fitted DMSP (2012–2017) data. Finally, a synthetic DMSP (2006–2017) dataset that can be used is constructed.

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Funding

This research is Supported by “the Fundamental Research Funds for the Central Universities,” Southwestern University of Finance and Economics (JBK2102035).

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Authors and Affiliations

  1. West Institute for Economic Research, Southwestern University of Finance and Economics, Liutai Avenue 555, Wenjiang District, Chengdu, 611130, China

    Xingmin Zhang & Pan He

  2. Research Institute of Economics and Management, Southwestern University of Finance and Economics, Chengdu, 611130, China

    Xiaoqian Liu

  3. Department of Applied Analytics, Columbia University, New York, NY, USA

    Tom Lu

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  1. Xingmin Zhang
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Contributions

Conceptualization, X. Z. and P. H. methodology, X. Z. Software, X. Z. and T. L. Validation, X. Z. and T. L. Formal analysis, X. Z. and P. H. Data curation, X. Z. and X. L. Writing—original draft preparation, X. Z. and X. L. Writing—review and editing, P. H. and X. L. Visualization, X. Z. and X. L. and T. L. Supervision, P. H. and X. L.

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Correspondence to Xiaoqian Liu.

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Appendices

Appendix 1. Spatial distribution of carbon emission performance

Based on Formulas (2) and (3), we measured the carbon efficiency and carbon intensity of each prefecture-level city from 2006 to 2017 and used ArcMap 10.7 to map the spatial distribution of carbon efficiency and carbon intensity in 2006, 2010, 2014, and 2017. The maps show that carbon efficiency and carbon intensity at the prefecture-level city in China have increased (declined) and both have significant spatial differences. The carbon efficiency of the eastern coastal region and the central and southwestern regions has increased significantly, and the spatial agglomeration has been further strengthened. The carbon emission intensity shows a distribution of high in the north and low in the south, and its spatial clustering characteristics are more obvious. Thus, there are significant spatial and temporal differences in both CO2 emissions and carbon efficiency at the prefecture-level city in China (Figs. 7 and 8).

Fig. 7
figure 7

The spatial distribution of carbon efficiency

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Fig. 8
figure 8

The spatial distribution of CO2 emission intensity

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Appendix 2. Parallel trends test and placebo test for CO2 emission intensity

Figures 9, 10, and 11

Fig. 9
figure 9

Parallel trend test results (WCI)

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Fig. 10
figure 10

Parallel trend test results (CI)

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Fig. 11
figure 11

Placebo test results of CO2 emission intensity

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Appendix 3. Balance test results of PSM-SDID

Tables 12 and 13

Table 12 Matching and balance test results (co2)
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Table 13 The pseudo R2 and joint statistical significance of the covariate propensity score (co2)
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Zhang, X., He, P., Liu, X. et al. The effect of low-carbon transportation pilot policy on carbon performance: evidence from China. Environ Sci Pollut Res 30, 54694–54722 (2023). https://doi.org/10.1007/s11356-023-25940-7

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