Multi-scenario simulation of emission reduction and subsidy scheme for dangerous goods vehicles

Abstract

Using trajectory data of dangerous goods vehicles (DGVs) in Chengdu, this study develops a three-phase framework to characterize spatio-temporal patterns of pollutant emissions, evaluate emission reduction benefits, and optimize subsidy policies. Results show that DGVs’ emissions exhibit a bimodal distribution, peaking between 9:30 and 11:30. These emissions expose over 3.2 million residents to health risks, with middle-income groups being the most affected. They lead to approximately 114 premature deaths and $18.68 million in annual economic losses. A combined strategy of improving fuel quality and electrifying heavy-duty DGVs could reduce over 76% of PM_2.5 emissions annually, prevent 84–93 premature deaths, and save $13.76–15.23 million. To close the cost gap and achieve optimal emission reduction benefits, this study recommends raising the subsidy cap for heavy-duty DGVs from $13800 to at least $21100 to promote electrification of China III or lower emission standard DGVs. Concurrently, charging infrastructure deployment should be accelerated.