Assessment of the macro-economic impacts of low-carbon road transportation policies in Chongqing, China

2020 
Abstract Reductions in the transportation sector’s carbon dioxide emissions are increasingly of global concern. As one of the first low-carbon pilot and carbon trading pilot cities, and as one of the largest automobile production bases in China, Chongqing has multiple low-carbon transportation policies that are coupled. In this study, three policy scenarios are set, including: 1) improving the fuel economy of newly sold gasoline passenger cars to 5.7 l per 100 km by 2020, 2) promoting pure electric private cars to increase the share to 7% of private car sales by 2020, and 3) the policy mix scenario of the above two policies. Simulations are undertaken with the Chinese Academy of Sciences general equilibrium (CAS-GE) model, a type of computable GE model, to assess the macro-economic impact and the industrial impact of the three policy scenarios. Through the policy impact mechanism analysis and data-mapping process, the micro-economic impact analysis results, including costs and fuel savings, for the two policies from the bottom-up model are taken as the shock variables and inputs for the CAS-GE model. The results show that: 1) the two policies will both have a slightly negative impact (−0.09% and −0.30%) on Chongqing’s GDP in 2020; 2) the employment rate will decrease by 0.12% and 0.47%, but the inflation rate will be restrained to a certain extent (−0.21% and −0.17%); and 3) the complementarity of the mixed policy can weaken the negative impact of the two policies when implemented separately. The mixed policy will reduce the GDP slightly by 0.37%, compared with the cumulative effect of the two policies implemented separately, resulting in cost-effective synergies at the macro-economic impact level; and 4) the COVID-19 pandemic in 2020 has an uncertain impact on the results. The method and results can provide a reference for the formulation and adjustment of low-carbon transportation policies in other large cities.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    38
    References
    2
    Citations
    NaN
    KQI
    []