*2.1. Global Change Assessment Model and GCAM-Korea*

GCAM is a community model which has been managed by the Joint Global Change Research Institute (JGCRI) for over 30 years. As a community model, GCAM is a fully open source code and model data on Github [25]. GCAM can investigate human-earth system dynamics alongside detailed representation of technology. The system consists of the economy, energy systems, agriculture and land-use, water, and the physical Earth system. As a partial equilibrium model based on a given socioeconomic pathway, GCAM finds equilibrium in the supply and demand of goods and services in each market and then determines market-clearing quantity and price [26,27]. GCAM models technology competition using the logit type of share equation based on the relative costs developed by McFadden [28]. The share of technology in each sector and period is changed smoothly by costs or policy changes [29]. That is, the logit share equation can prevent the winner-takes-all phenomenon which can be caused by an abrupt and slight price change in linear programming optimization [30,31].

Population and GDP (Gross Domestic Product) are exogenous inputs and driving forces for determining final energy service demand in conjunction with the cost of energy services and sector-specific energy services' price elasticity. The model is calibrated for energy consumption and pollutant emissions at the base year. In GCAM, GDP can affect future emissions of air pollutants. Smith et al. [32] examined the relationship between sulfur dioxide emission reduction and GDP per capita in Purchasing Power Parity (PPP) in 17 world regions from 1850 to 2000. Their study developed an income-based parameterization for an IAM to control sulfur dioxide emissions. Based on their study, GCAM adopted the income-based emission control function for NOx and SOx. As a result, fast economic growth tends to implement emission reduction rapidly. In GCAM, anthropogenic air pollutant emissions are driven not only by fuel consumption but also GDP per capita.

While GCAM's energy-economy system presents 32 regions globally including South Korea as a separate region, the recent GCAM represents various spatial resolutions for capturing the heterogeneity of certain regions which have not been modeled separately. As an example of a country-specific GCAM, which was not modeled as a separate region before, GCAM-Ethiopia was developed by separating Ethiopia from Eastern Africa that is one of the 32 global regions to go over biomass policy effects on Ethiopian energy consumption [33]. GCAM-Gujarat is a bit more detailed country GCAM. GCAM-Gujarat is an extended version of GCAM-India and was used for assessing building energy policies in Gujarat state in India [34]. GCAM-China has a higher resolution, which represents 31 provinces in China with other global regions. GCAM-China was used for examining the role of technologies such as carbon dioxide capture, utilization, and storage (CCUS) [35] and nuclear power plants [36] in China at the provincial level. Another example of higher spatial GCAM is GCAM-USA, which subdivided the USA region into 50 US states and D.C. and was also used as a PM2.5 analysis tool for US states and D.C. Shi et al. [17] projected NOx, SO2, and PM2.5 emissions, and Ou et al. [20] estimated PM2.5 mortality costs.

GCAM-Korea is developed based on GCAM-USA ver. 5.1.3 for investigating the South Korean energy system at the provincial level. GCAM-Korea subdivides South Korea into 16 provinces except for Sejong (Figure 1). As Sejong is a relatively new city established in 2012 and it has only 0.5% of South Korea's residents not enough information is available on the region as yet. In GCAM-Korea, 31 global regions outside South Korea interact with 16 provinces in South Korea. Socioeconomics and energy systems are represented at the provincial level, while land-use and water systems adopt the default GCAM system. Although GCAM-Korea operates in 5-year periods from 2010 to 2040, the operation period can be extended through further modeling work. The base year is 2010 for calibration of energy and emissions. Input data for GCAM-Korea is available at GitHub (https://github.com/rohmin9122/gcam-korea-release) [37].

GCAM-Korea exhibits the provincial features of the energy sector. Electricity from coal power plants is mostly generated in Chungnam and Gyeongnam. Electricity is mainly consumed by the building and industrial sectors which are mostly located in the Seoul metropolitan area, Gyeonggi, Chungnam, and Jeonbuk; 77% of the national industrial energy is consumed in four provinces: Jeonam, Chungnam, Ulsan, and Gyeongbuk. Energy consumption in the building and transportation sectors is intensive in the Seoul metropolitan area which accounted for 52% and 44% of the total energy consumption in the building and transportation sectors, respectively, in 2015.

**Figure 1.** Administration divisions in Korea [37].

## *2.2. Modeling Air Pollutant Emissions in GCAM-Korea*

As the current GCAM-Korea is modeled only for socioeconomics and energy systems, air pollutant emissions modeling is a new feature which requires to be augmented. Hence, this study further develops GCAM-Korea by using air pollutant emissions data from the national air pollutant emissions inventory.
