Transboundary Particulate Matter Pollution and the Environmental Kuznets Curve in Korea

I. Introduction

Improving air quality in South Korea has long been a central issue for the government. The PM concentration of South Korea’s capital, Seoul, hit a concentration of 963µg m^-3 in December 2009 and reached a staggering record of 1044µg m^-3 in 2015 (Park, 2018). This is an alarming value considering the World Health Organization (WHO)’s air quality guideline value for PM₁₀ which is set at 15µg m^-3 for the annual mean and 45µg m^-3 for the 24-hour mean. The government’s stringent air quality regulations succeeded in decreasing the concentration levels of PM in the early 2000s (Song, 2019), but the fine dust concentration in South Korea still exceeds the WHO’s air quality guidelines and remains at the bottom (35th out of 38 countries) among the Organization for Economic Co-operation and Development (OECD) member countries (Park, 2018).

Domestically, the Ministry of Environment recently implemented the Fourth Fine Dust Seasonal Management System in December 2022, with goals to reduce coal-fired power plant operations and abolish old power plants (Ministry of Environment, 2022). Attempts at international cooperation to mitigate air pollution have also been present. In August 2022, the Ministry of Environment and the Ministry of Ecology and Environment of the People’s Republic of China jointly disclosed their plans to enhance international cooperation by continuously implementing the Korea-China Cheongcheon Plan and jointly publicizing the achievements of fine dust reduction between Korea and China (Ministry of Environment, 2022).

Regardless, it is evident that South Korea has yet to satisfy both national and international air quality guideline goals. Over the years, the environmental ramifications of the country’s rapid economic growth have become an impetus for countless research and studies. The copious research includes studies on the environmental Kuznets curve (EKC) of a country, which looks at the relationship between economic growth and environmental degradation.

The EKC hypothesis proposed by Grossman and Krueger (1993) suggests an inverted U-shaped relationship between economic growth and environmental degradation. This means that as an economy starts moving along the growth trajectory, the environment first deteriorates due to pollution and other environmental consequences attributed to rapid industrialization, and then starts to improve when the economy starts to develop and reaches a particular income level. Unlike this hypothesis, recent research (Allard et al., 2018; Kang, 2019) suggests an N-shaped relationship between South Korea’s environmental degradation and economic growth, with air pollution as the environmental indicator. This implies that although environmental degradation has been alleviated at some point, it has again started to rise. One plausible explanation for the rise of environmental degradation may be the transboundary factors, for example, that of the neighboring country China. Although empirical studies and reports (Ministry of Environment, 2017; Jia and Ku, 2019; Park et al., 2020) have long mentioned the effects of China’s transboundary air pollutants on South Korea’s air quality, there has not been any precedent research that has included China’s effect to derive South Korea’s EKC.

This paper attempts to answer the following question: What shape would South Korea’s environmental Kuznets curve depict with China’s transboundary air pollution effect? To answer this question, this study investigates the relationship between economic growth and South Korea’s PM₁₀ concentration and includes PM concentrations monitored at South Korea’s coastal monitoring stations as a proxy variable of the China’s effect.

This paper include two groups of control variables according to previous studies. The one is climate factors such as rainfall and westerly wind. Rainfall negatively affects the PM concentrations with removal of material from the atmoshphere by hydrometers (Yoo et al., 2020; Liu et al., 2020) and westerly wind intensifies transboundary transport effects (Chae, 2009; Sim, 2019; Jun and Gu, 2023). The other is local factors such as registered diesel vehicles in Seoul and diesel price in Seoul. The increase in diesel prices has the effect of reducing PM concentrations by decreasing diesel consumption (Lee, 2010; Bae and Kim, 2016; Cho 2020). The number of diesel vehicles has positive effect on the PM₁₀ concentration (Kang, 2019).

This paper employs generalized least squares (GLS) to derive EKC and adopts seemingly unrelated regression (SUR) to visualize the relationship between PM and other major air pollutants in South Korea; sulfur dioxide (SO₂) and nitrogen dioxide (NO₂). The result shows an N-shaped trajectory EKC for South Korea’s PM₁₀ pollution with turning points at $7,964 and $12,897, implying that South Korea’s per capita GDP has passed the second turning point and PM₁₀ concentration increase as income increases. This paper further finds South Korea’s major air pollutants are cointegrated.

The remainder of the paper is organized as follows. Section 2 introduce the relevant literature on EKC. Section 3 describes the data and empirical model. Section 4 provide the empirical results of the EKC analysis. Section 5 offers concluding remarks and corresponding implications.

Ⅱ. Literature Review

Originated from Simon Kuznets (1985)’s proposal about an inverted U-shaped relationship between inequality and economic growth, Grossman and Krueger (1993) found its resemblance with the relationship between a country’s environmental degradation and economic growth. Since Grossman and Krueger (1993), a large body of research empirically investigates the validity of the EKC hypothesis for many environmental indicators such as suspended particles (Grossman and Krueger, 1993; Selden and Song, 1994), water pollutants (Grossman and Krueger, 1995; Cole, 2004), solid waste (Dinda, 2004; Diao et al., 2009), and deforestation (Cropper and Griffiths, 1994; Bhattarai and Hammig, 2001) using several explanatory variables such as trade (Grossman and Krueger, 1993; Shafik and Bandyopadhyay, 1992; Atici, 2012), energy consumption (Shafiei and Salim, 2014; Zhang et al., 2017), urbanization (Shafiei and Salim, 2014; Pata, 2018), and population density (Lantz and Feng, 2006; Salim et al., 2019).

For previous studies on South Korea’s EKC, Cho et al. (2001), Choi and Kim (2006), Rhee (2016), and Dong et al. (2018) conduct multiple country analyses including South Korea and employ carbon dioxide (CO₂) as the common environmental indicator. However, the earlier single-country studies on EKC of South Korea use multiple air pollutants such as total suspended particulates (TSP), sulfur oxides (SO, SO₂), and nitrogen oxides (NO, NO₂). Kim and Oh (2005) compare the EKC shapes and patterns and Kim (1999) shows air and water pollutants together. Lee and Li (2009), Lee (2010), Bae and Kim (2012) and Park and Park (2019) examine the relationship between economic growth and CO₂ emissions. The findings of those studies show conflicting results for EKC’s shapes and significance. Lee and Lee (1996), Kim et al. (1998) and Kim (2002) collected regional TSP data and found an inverted U-shaped EKC with turning points occurring at different economic levels.

With the start of the monitoring of PM₁₀ in 1995, particulates in the air were separated from total suspended particulates. Kim and Kim (2008) and Yoon and Han (2010) explored South Korea’s municipalities and economic regions using multiple air pollutants including PM₁₀ and found both inverted U and U-shaped EKC relationships. Kang (2019) examined the EKC hypothesis for 16 economic regions in South Korea during the period 2003-2014 using gross regional domestic product (GRDP) as the economic indicator and PM₁₀ as the environmental indicator, and showed an N-shaped relationship between air pollution caused by PM₁₀ and economic development with the spatial Durbin model (SDM).

Table 1 summarizes prior literature for South Korea’s EKC. In Table 1, turning points of South Korea’s EKC happen faster for TSP than CO₂. A possible explanation for this result is that the government started taking measures to mitigate TSP before greenhouse gas emissions problems started to rise globally, explaining the early turning points for TSP than CO₂. Empirical evidence of the shape of South Korea’s EKC in Table 1 is mixed and a plausible explanation is due to the possibility of transboundary factors.

<Table 1>

The summary of EKC literature for South Korea

Related with transboundary factor, Sim (2019) reported that long-range transboundary transport of air pollutants has also been affiliated with the PM₁₀ concentration levels of South Korea. Several research showed that transboundary transport effects intensify under meteorological conditions such as westerly winds from China to Korea (Sim, 2019; Jun and Gu, 2023).

Studies on meteorologic factors affecting air pollutant concentration suggest that the rainfall and westerly wind have also been associated with the concentration levels of air pollutant. Chae (2009) investigated the relationship between wind speed and direction with PM10 concentrations and found that westerly wind passing through China’s most industrially intense regions is associated with high concentration levels of PM10. Yoo et al. (2020) and Liu et al. (2020) found that rainfall negatively affects the PM concentrations due to the precipitation scavenging effect, which is the removal of material from the atmosphere by hydrometeors (Loosmore and Cederwall, 2004). The Korea Energy Economic Institute has also reported that days of rainfall affects the PM levels (Sim, 2019), in line with Yoo et al. (2020) and Liu et al. (2020). Related with local factors such as energy price and the number of registered vehicles, Lee (2010), Bae and Kim (2016) and Cho (2020) showed that increasing energy prices negatively affect air pollutant through the effect of curbing energy demand. Kang (2019) found that the number of registered diesel cars increases the PM10 concentration in the registered area.

Ⅲ. Data and Methodology

1. Data

In this paper, monthly time series data was used and spans from January 2000 to December 2021. This study takes the PM₁₀ concentrations of Seoul as the dependent variable because Seoul has social significance and the results of the atmospheric diffusion model suggest that Seoul is affected by PM from China. Monthly average Seoul’s PM₁₀ concentration measured in µg m^-3, denoted by SeoulPM was retrieved from Air Korea website, which was launched by the Korean Ministry of Environment to provide air pollution information.

Economic growth variables and the transboundary factor were included as main explanatory variables in addition to weather and local factors. Following the traditional EKC literature, per capita GDP was used as the indicator for South Korea’s economic growth (Shafik and Bandyopadhyay, 1992; Panayotou, 1993; Grossman and Krueger, 1995). Per capita GDP measured in 2015 US dollars, denoted by GDPPC, was retrieved from the OECD statistics database.

According to reports that long-range transboundary transport of air pollutants has also been affiliated with the PM₁₀ concentration levels of South Korea, this paper employs China’s transboundary air pollution effect as transboundary factor (Sim, 2019; Jun and Gu, 2023). As proxy of China’s transboundary air pollution, the average PM₁₀ concentration measured at the Seongmori, Padori, and Songhae monitoring stations, denoted by ChinaPM, is used. The data is measured in µg m^-3 and is retrieved from the Air Korea website. The Baengnyeongdo air pollution monitoring station located in the middle of Korea and China is the most appropriate monitoring station to extract data from outside South Korea. Since there are no factories or much traffic on the Baengnyeongdo island, its monitoring station has been built and utilized to monitor international air pollution effects. However, unfortunately, it was only built in 2012, providing insufficient data for our analysis from 2000 to 2021. In place, the PM₁₀ concentration monitored at the Seongmori, Padori, and Songhae is adopted in the analysis because these monitoring stations are all located on the Northwestern coastline of the South Korea as shown in figure 1.

<Figure 1>

Map of westerlies and coastal monitoring stations

Based on previous studies, the days of rainfall and days of westerlies are employed as weather factor, and the price of diesel and the ratio of registered diesel vehicles are adopted as local factor (Lee, 2010; Bae and Kim, 2016; Chae, 2009; Kang, 2019; Sim, 2019; Zhou et al., 2020; Yoo et al., 2020). The days of rainfall, denoted by Rainfall, refers to the number of days per month where the daily amount of precipitation was greater than or equal to 0.1mm in Seoul. The days of westerlies, denoted by Wind, refers to the number of days the westerly wind has been observed in Baengnyeongdo each month. Here, the westerly wind is defined as wind blowing from between 225~315° in a 16-wind direction geographic coordinate system, following the works of Kim et al. (2022). The data for rainfall and westerly wind are retrieved from the Korea Meteorological Administration database. The price of diesel, denoted by Diesel_p, refers to the average price of diesel in Seoul and is provided from Korea national oil corporation. The ratio of registered diesel vehicles, denoted by Diesel_r, is defined as the ratio of registered diesel cars to the total number of cars registered in Seoul. The data for registered vehicles is retrieved from Ministry of Land, Infrastructure and Transport.

To explore relationship between South Korea’s main air pollutants using the SUR regression model, average SO₂ and NO₂ concentration of Seoul, denoted by SeoulSO₂ and SeoulNO_2, are used as other ambient air pollutants. Average SO₂ and NO₂ concentration of Seoul measured in 1000ppm were retrieved from Air Korea. Averages of SO₂ and NO₂ concentration monitored at Seongmori, Padori, and Songhae, denoted by ChinaSO₂ and ChinaNO_2, is the same mechanisms as ChinaPM.

The summary of the variable definition is presented in Table 2 and the descriptive statistics of the variables and the results of unit root test are displayed in Table 3.

<Table 2>

Variable definition

<Table 3>

Descriptive Statistics

Ⅳ. Results and Discussion

1. GLS Estimation Results

Table 4 shows the GLS estimation results for Seoul’s PM₁₀. The sign of the GDPPC, its quadratic and cubic term for South Korea’s EKC with China’s PM effect depict an (+), (-), (+) pattern at significance levels 5% or higher and β₂² - 3β₁β₃ is positive. Thus, this result shows that South Korea’s current EKC between PM₁₀ and economic growth, with several factors including China’s influence, depicts an N-shaped relationship. The sign of Wind is positive and this results are consistent with Chae (2009), Sim (2019), and Jun and Gu (2023).

<Table 4>

The effects of China’s PM on South Korea’s EKC

ChinaPM has a positive influence on Seoul’s PM₁₀ concentration at a significance level of 1%. This result is a strongly support for our claim that China’s transboundary air pollution effect has the rise of Seoul’s air pollution. The sign of the GDPPC, its quadratic and cubic term for South Korea’s EKC without China’s PM effect show the same pattern of (+), (-), (+) and β₂² - 3β₁β₃ is positive, indicating an N-shaped EKC just like the case of EKC with China’s effect. However, all coefficient except for GDPPC², Wind and price of diesel do not have a statistically significant effect on Seoul’s PM₁₀ concentration. Wind has a significantly negative effect and the price of diesel is positively effect on the concentration of ambient air pollution. Our findings suggest South Korea’s EKC derives a significant N-shape trajectory EKC by adding China’s effect.

Figure 2 plots the graph of both of the EKCs derived by the GLS analysis. For the EKC with the China effect, the first turning point occurred at $7,964 GDPPC, and the second at $12,897. The first turning point GDPPC value derived in this analysis is fairly close to the turning points derived in previous research on South Korea using TSP (Kim, 2002). However, the turning points of PM₁₀ arrive at a much earlier point of economic development compared to the turning points of the EKC derived for South Korea’s CO₂ (Lee and Li, 2009; Lee, 2010; Bae and Kim, 2012; Park and Park, 2019). The EKC for South Korea without the China’s effect is drawn for visual comparison with the EKC with China effect, although estimates of per capita GDP for South Korea’s EKC without China’s effect are insignificant.

<Figure 2>

EKC with and Without China Effect

2. SUR Estimation Results

Table 5 shows the SUR estimation results for PM₁₀, SO₂, and NO₂. The coefficient values for the PM₁₀ estimates in the SUR model are almost identical to the GLS estimation results in Table 4, with slight changes in the values of the coefficients.

<Table 5>

The effects of China’s PM10, SO2, NO2 on South Korea’s EKC

The signs of the GDPPC, its quadratic and cubic term for dependent variable “SO₂” is the same as those for dependent variable “PM₁₀”, showing a pattern of (+), (-), and (+), however, statistically insignificant. The Rainfall is significantly negative effect on SO₂ but the Wind is significantly positive effect on SO₂. This results suggest that Rainfall and Wind are both climate factors affecting ambient air pollution. Although the China’s effect of SO₂ concentration is smaller than that of PM₁₀ concentration, China’s air pollution effect is also significantly visible in the case of SO₂.

On the other hand, in the case of NO₂, all the explanatory variables show a strong significance level except for the diesel price. The signs of the GDPPC, its quadratic and cubic terms show a (-), (+), (-) pattern, but β₂² - 3β₁β₃ is negative. This implies that NO₂ concentrations decreases as GDPPC increases. Besides the per capita GDP, the Rainfall has a significantly negative effect and the Wind has significantly positive effect, which is consistent with results of the other cases. Finally, China’s NO₂ effect shows a significant positive value. In the SUR model, the China’s effect of NO₂ had the greatest value among the three pollutants (1.513 > 1.047 > 0.698), suggesting that China’s air pollution effects may have a greater impact on South Korea’s NO₂ concentrations than PM₁₀ concentrations.

The chi-square (χ²) values were 2282.63 for PM₁₀ concentration, 688.55 for SO₂, and 920.89 for NO₂ concentration, which were all significant at 1% significance level. Those values imply that the SUR model is able to explain a great proportion of the relationship between the variables.

The correlation matrix of the residuals from the SUR estimation is summarized in Table 6. The correlation coefficient of PM₁₀ and SO₂ is valued at 0.304 and the correlation coefficient of PM₁₀ and NO₂ was valued at 0.269, both showing a relatively weak correlation. The results also indicate that the NO₂ and SO₂ concentration has a stronger correlation, compared with that of PM₁₀, having a correlation coefficient of 0.371. The chi-squared value from the Breusch-Pagan test of independence was valued at 76.958 with a p-value less than 1% level of significance, indicating presence of heteroskedasticity in the regression model.

<Table 6>

Correlation matrix of residuals

Ⅴ. Conclusion

Using monthly time-series monthly data covering from January 2000 to December 2021, this study attempts to investigate the effect of China’s PM, an international transboundary factor on South Korea’s air pollution within the framework of South Korea’s environmental Kuznets curve (EKC). This paper contributes to previous literature in two ways; i) it attempts for the first time to use transboundary factors to derive South Korea’s EKC for air pollution using the most recent data available and ii) that comprehensive national air pollution measures is considered

Including China’s air pollution effect in South Korea’s PM₁₀ EKC derives an N-shaped trajectory EKC, with turning points first at $7,964 and then the second at $12,897 GDP per capita. This finding suggests that South Korea’s per capita GDP has passed the second turning point and PM₁₀ concentration increase as income increases. The result also shows that South Korea’s EKC is statistically insignificant when China’s effect is not considered. The finding implies that China’s effect plays a role in the process of improving and deteriorating air quality in South Korea and that China’s effect should be considered when suggesting the direction of the policy to mitigate air pollution.

Another important implication is that comprehensive national air pollution measures must be met in South Korea. The SUR analysis shows that PM₁₀, SO₂, and NO₂ are cointegrated, and are not completely independent. Therefore, comprehensive air quality improvement measures may be more effective measures that target single air pollutants.

This study only provides preliminary empirical evidence with limitations. First, given the lack of monthly data, some important indicators such as urbanization and population density were not considered in the study. Also due to the lack of data, proxy variables were composed and utilized in place of China’s PM concentrations, and PM₁₀ concentrations were used as environmental indicators instead of PM_2.5, which are pollutants considered to have greater health risks to humans. Therefore, adding an other indicator to the econometric model and exploring the socioeconomic drivers of PM₁₀ emissions may lead to interesting results in future research. Second, as this study was the first attempt to include an international transboundary factor in the framework of EKC, the GLS model were used, ignoring some of the important aspects of air pollution considered in previous air pollution EKC studies, such as spatial autocorrelation and time lags.

Acknowledgments

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea.(NRF-2022S1A5C2A03093594).

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