Reduction measures for air pollutants and greenhouse gas in the transportation sector: A cost-benefit analysis

Abstract Cost-benefit analysis of emission control measures is an important part of the development and refinement of policies in the transportation sector, especially in China. In this paper, a cost-benefit analysis of the measures in place to reduce air pollutants [carbon monoxide (CO), volatile organic compounds (VOCs), nitrogen oxides (NO X ), fine particulate matter (PM 2.5 )] and greenhouse gases (GHGs) [carbon dioxide (CO 2 )] in the Pearl River Delta (PRD) region by 2020 is conducted. Based on the design of five types of emission reduction scenarios and normalization of the emission reduction effect, the total costs and the cost-benefit of these reduction scenarios in the PRD region by 2020 are evaluated. Results show the scenario, which new cars bought after 2014 are all upgraded from China V emission standard to China VI emission standard and motorcycles are still China III emission standard (UES), has a significant effect with 717,690 tons of the air pollutants equivalent and 38,960 tons of the GHGs equivalent reduction, while the Eliminate Motorcycles (EM) scenario produces the worst emissions reduction result. Therefore the cost-benefit of the UES scenario is extremely high, and the unit cost of air pollutants and GHGs reductions is only 0.003 Renminbi/g (RMB/g), showing a win-win situation of emission reduction and cost savings. Although the total cost of the BPSevere scenario, giving priority to public transportation development to reduce the mobility for private cars and motorcycles, is the highest (197.47 billion RMB), it can effectively reduce 9925 tons of GHGs equivalent and bring significant climate benefits. Moreover, for any reduction scenarios, the cost-benefit cannot be improved by strengthening the control level. And the comparison of the unit reduction costs among four different sectors shows that the cost-benefits of reduction scenarios in the transportation sector are worse and more variable.