**5. Conclusions and Policy Implications**

This study modeled air pollutant emissions using GCAM-Korea focusing on the road transportation sector. The projected emissions compared to the national emissions inventory using GCAM-Korea works fairly well with empirical data across sectors and provinces except for VOC from LDV2W in which the reported emissions in the emissions inventory contradict energy use.

The study applied the extended GCAM-Korea with air pollutant emissions modeling for examining the ZEV subsidy's effects on emission reductions for PM2.5 as well as its precursors. Subsidy scenarios based on the current policy are found to have a major impact on the LDV4W sector in terms of change in service demand and emission reduction, whereas it is expected to have a minor impact on the other sectors. In all the scenarios, the government's target of ZEVs' dissemination is expected to be not attainable. The resulting expected emission reductions of PM2.5 are 0.6–1.2% in the Sunset case and 0.6–4.1% in the NoSunset case compared to the baseline. The Seoul metropolitan area contributes 38–44% of the total emission reductions. Chungcheong province is the second most mitigated province next to the Seoul metropolitan area because of the second and third largest subsidy for BEVs in the LDV4W sector, even though this province has relatively low traffic and a small population compared to metropolitan areas. Its emission reduction accounts for 17–21% and 17–20% of the overall emission reductions in the Sunset and the NoSunset cases respectively. NH3 is the most mitigated pollutant, for which the emission reduction rate is 1.7–3.7% in the Sunset case and 1.7–12% in the NoSunset case. On the other hand, NOx emissions are expected to reduce very slightly with an emission reduction rate of 0.2–0.5% and 0.2–1.7% in the Sunset and NoSunset cases respectively.

As the ZEVs subsidy is weighted towards the LDV4W sector, as is shown in Table 5, various spillover effects are found: ZEVs' share rises intensively in the LDV4W sector, which leads to an increase in its service costs, while this drives the bus service costs to become relatively cheaper. This whole process, in turn, drives an increase in bus service demand and emissions. In other

words, an imbalanced ZEVs subsidy distribution may dampen the subsidy's effect on air pollution improvements. Furthermore, the ZEVs subsidy is not expected to reduce ICEVs in the truck sector, although diesel freight trucks are a major contributor to PM2.5 emissions as also NOx. This means targeting emission reduction by promoting ZEVs might be misleading without explicit consideration of ICEVs in the truck sector. Another finding is that the decline in emissions over time without any policy is more than the ZEV subsidy's effects.

As this analysis does not cover uncertainty in the total costs of ZEVs, this should be considered in a future study. While infrastructure costs increase ZEVs' total costs, incentives for charging station installations and tax incentives for buyers decrease costs. Moreover, total costs can vary under future trends of efficiency and costs. The amount of ZEV purchase subsidy for the future is also uncertain because the government has not decided on this as yet. The uncertainty around cost eventually influences ZEVs' service demand, which changes the effects of the ZEV subsidy policy on air quality mitigation. In addition, emissions caused by increasing electricity and hydrogen consumption for ZEVs should also be considered from the perspective of the entire energy system. Emissions modeling for other sectors such as power generation and industry sectors will be conducted which is expected to provide more meaningful implications for cross-sector and cross-province aspects in the future.

**Author Contributions:** Conceptualization, M.R., S.J., S.K. (Suduk Kim), S.K. (Soontae Kim), and S.Y.; Methodology, M.R. and S.J.; Software, M.R. and S.J.; Data curation, M.R. and S.J.; Visualization, M.R.; Writing—Original Draft Preparation, M.R.; Writing—Review & Editing, S.K. (Suduk Kim), S.Y., S.K. (Soontae Kim) and A.H.; Supervision, S.K. (Suduk Kim); Funding acquisition, S.K. (Suduk Kim). All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Technology Development Program to Solve Climate Changes of the National Research Foundation (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2017M1A2A2081253), and the Korea Ministry of Environment (MOE) as Graduate School specialized in Climate Change.

**Acknowledgments:** We would like to thank Jaeick Oh, Jaesung Jung, and three anonumous reviewers for their valuable comments.

**Conflicts of Interest:** The authors declare no conflict of interest.
