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We study the design of fair international protocols for the abatement of GHG emissions. We formulate normative principles, pertaining to countries' population, emission history, and business as usual emissions, as axioms for allocation rules. We show that combinations of these axioms characterize the so-called equal per capita allocation rules, with or without historical accountability. The allocations provided by these rules are in stark contrast with the allocation suggested by the Kyoto Protocol, which is close to the allocation in proportion to the current and business-as-usual emissions, suggested by the equal per emission (grandfathering) rule. As we illustrate, the equal per capita allocations admit more emissions to developing countries with large populations. And, with historical accountability, developed countries with large historical emissions are clearly penalized.
Biung-Ghi Ju; Min Kim; Suyi Kim; Juan D. Moreno-Ternero. Fair international protocols for the abatement of GHG emissions. Energy Economics 2021, 94, 105091 .
AMA StyleBiung-Ghi Ju, Min Kim, Suyi Kim, Juan D. Moreno-Ternero. Fair international protocols for the abatement of GHG emissions. Energy Economics. 2021; 94 ():105091.
Chicago/Turabian StyleBiung-Ghi Ju; Min Kim; Suyi Kim; Juan D. Moreno-Ternero. 2021. "Fair international protocols for the abatement of GHG emissions." Energy Economics 94, no. : 105091.
We examined the effects of oil prices along with fundamental economic variables on exchange rate movements in the Korean and Japanese foreign exchange markets, using two-regime Markov Regime Switching Models (MRSMs) over the period from January 1991 to March 2019. We selected the best MRSMs explaining their exchange rate movements using the Maximum Log-Likelihood and Akaike Information Criteria, and analyze effects of oil prices on their exchange rates based on the selected best MRSMs. We consider two regimes, regime 1 with high-volatility and regime 2 with low-volatility. In Korea, two apparent regimes are observed, and unstable regime 1 consists of two distinct prolonged periods, the 1997 Asian Financial Crisis and the 2008 Global Financial Crisis. Meanwhile in Japan, no evident prolonged regimes are observed. Rather, the two regimes occasionally alternate. Oil prices influence exchange rate movements in regime 2 with low-volatility in Korea, while they do not influence exchange rate movements in either regimes in Japan. The Japanese foreign exchange market is more resistant to external oil price shocks because the Japanese industry and economy has less dependence on oil than Korea.
Suyi Kim; So-Yeun Kim; Kyungmee Choi. Effect of Oil Prices on Exchange Rate Movements in Korea and Japan Using Markov Regime-Switching Models. Energies 2020, 13, 4402 .
AMA StyleSuyi Kim, So-Yeun Kim, Kyungmee Choi. Effect of Oil Prices on Exchange Rate Movements in Korea and Japan Using Markov Regime-Switching Models. Energies. 2020; 13 (17):4402.
Chicago/Turabian StyleSuyi Kim; So-Yeun Kim; Kyungmee Choi. 2020. "Effect of Oil Prices on Exchange Rate Movements in Korea and Japan Using Markov Regime-Switching Models." Energies 13, no. 17: 4402.
This study analyzes the effects of foreign direct investment (FDI), economic growth, industrial structure, renewable and nuclear energy, and urbanization on Korean greenhouse gas (GHG) emissions from 1981 to 2014. The cointegration relationship of the variables is examined using autoregressive distributed lag (ARDL) bounds test. The test confirmed the long-run equilibrium among the variables. After that, the short-run and long-run coefficients are estimated by an ARDL error-correction model. The result shows that in the long run, economic growth and urbanization are the main contributors to the increase of GHG emissions, while manufacturing industry share, renewable energy and nuclear energy contributed to the reduction of GHG emissions. The inflow of FDI has led to the increase of greenhouse gases, but the coefficients is negligible. In the short run, economic growth has caused an increase in GHG emissions, while renewable and nuclear energy have contributed to the reduction in GHG emissions. FDI and urbanization did not play a role in increasing of GHG emissions in the short term.
Suyi Kim. The Effects of Foreign Direct Investment, Economic Growth, Industrial Structure, Renewable and Nuclear Energy, and Urbanization on Korean Greenhouse Gas Emissions. Sustainability 2020, 12, 1625 .
AMA StyleSuyi Kim. The Effects of Foreign Direct Investment, Economic Growth, Industrial Structure, Renewable and Nuclear Energy, and Urbanization on Korean Greenhouse Gas Emissions. Sustainability. 2020; 12 (4):1625.
Chicago/Turabian StyleSuyi Kim. 2020. "The Effects of Foreign Direct Investment, Economic Growth, Industrial Structure, Renewable and Nuclear Energy, and Urbanization on Korean Greenhouse Gas Emissions." Sustainability 12, no. 4: 1625.
Korea imports all of its crude oil, and is the world's fifth largest oil importing country. We analyze the effects of oil prices, interest rates, consumer price indexes (CPIs), and industrial production indexes (IPIs) on the regime shift behavior of the Korean exchange rates against the USA from January 1991 to March 2019. We use the Markov regime switching model (MRSM) to detect the regime shift behavior of the movements of Korean exchange rates. In order to select the optimal MRSM, we fit a total of 30 models considering four explanatory variables. The selected model based on Akaike information criteria (AIC) and maximum log likelihood (MLL) includes the log-differentials of oil prices, the log-differentials of CPIs compared to those of the US, and its own auto-regressive terms. Based on the selected MRSM model, throughout all markets, we find evidence to support the existence of two distinct regimes: a stable regime with low-volatility, and an unstable regime with high-volatility. The regime with high-volatility includes the Asian financial crisis of 1997 and the global financial crisis of 2008–2009 in the Korean exchange rates market. In the regime with low-volatility, the Korean exchange rates are not significantly influenced by any of the explanatory variables, except for its own auto-regressive terms. In the regime with high-volatility, the Korean exchange rates are significantly influenced by the CPIs and oil prices. The transition probability from the regime with low-volatility to the regime with high-volatility is about ten times that of the opposite case.
So-Yeun Kim; Kyungmee Choi. Analyzing Oil Price Shocks and Exchange Rates Movements in Korea using Markov Regime-Switching Models. Energies 2019, 12, 4581 .
AMA StyleSo-Yeun Kim, Kyungmee Choi. Analyzing Oil Price Shocks and Exchange Rates Movements in Korea using Markov Regime-Switching Models. Energies. 2019; 12 (23):4581.
Chicago/Turabian StyleSo-Yeun Kim; Kyungmee Choi. 2019. "Analyzing Oil Price Shocks and Exchange Rates Movements in Korea using Markov Regime-Switching Models." Energies 12, no. 23: 4581.
Hong Kong, Singapore, South Korea, and Taiwan are called the Asian Newly Industrialized Countries (ANICs), which were famous for maintaining exceptionally high growth rates (in excess of 7% per a year) and attaining rapid industrialization between the early 1960s (mid-1950s for Hong Kong) and the 1990s. These countries have a comparative advantage in producing different types of products at different stages in their economic development. As such, their economic success stories have served as role models for numerous developing countries.
Suyi Kim. CO$$_{2}$$ Emissions, Energy Consumption, GDP, and Foreign Direct Investment in ANICs Countries. Contemporary Issues in Applied Economics 2019, 343 -360.
AMA StyleSuyi Kim. CO$$_{2}$$ Emissions, Energy Consumption, GDP, and Foreign Direct Investment in ANICs Countries. Contemporary Issues in Applied Economics. 2019; ():343-360.
Chicago/Turabian StyleSuyi Kim. 2019. "CO$$_{2}$$ Emissions, Energy Consumption, GDP, and Foreign Direct Investment in ANICs Countries." Contemporary Issues in Applied Economics , no. : 343-360.
This study analyzed the greenhouse gas (GHG) emissions from the transportation sector in Korea from 1990 to 2013 using Logarithmic Mean Divisia Index (LMDI) factor decomposition methods. We decomposed these emissions into six factors: The population effect, the economic growth effect due to changes in the gross domestic product per capita, the energy intensity effect due to changes in energy consumption per gross domestic product, the transportation mode effect, the energy mix effect, and the emission factor effect. The results show that some factors can cause an increase in GHG emissions predominantly influenced by the economic growth effect, followed by the population growth effect. By contrast, others can cause a decrease in GHG emissions, predominantly via the energy intensity effect. Even though the transportation mode effect has contributed to a reduction of GHG emissions, it remains relatively small compared to other factors. The energy mix and emission factor effects contributed to the reduction of GHG emissions in the early 2000s, however the effects have led to an increase of GHG emissions since the mid-2000s. Altogether, based on these results, this study suggests some GHG mitigation policies aimed at achieving the national target for this sector.
Suyi Kim. Decomposition Analysis of Greenhouse Gas Emissions in Korea’s Transportation Sector. Sustainability 2019, 11, 1986 .
AMA StyleSuyi Kim. Decomposition Analysis of Greenhouse Gas Emissions in Korea’s Transportation Sector. Sustainability. 2019; 11 (7):1986.
Chicago/Turabian StyleSuyi Kim. 2019. "Decomposition Analysis of Greenhouse Gas Emissions in Korea’s Transportation Sector." Sustainability 11, no. 7: 1986.
Since the Asian financial crisis and the global financial crisis, the regime shift behavior has been notable in the stock markets. We examine the effects of interest rates and foreign exchange rates on stock returns and the cross-correlations of Korean stock returns associated with three other countries: Japan, USA, and China, using the Hamilton 2-regime Markov Switching model, for the period January 1993–December 2016. In both regimes, the volatility in the Korean stock market is greater than Japan and USA, but less than China. In regime 1 with low-volatility, the stock returns of both Korea and Japan are significantly affected first by their exchange rates and then by their interest rates. In regime 2 with high-volatility, the Korean stock market is explained by neither of the two exogenous variables while the Japanese stock returns respond positively to the exchange rates but negatively to the interest rates. The transition probability from regime 1 to regime 2 is greater than the reverse probability in the Korean stock market, which is opposite in Japan. Considering all four countries simultaneously, the Korean stock market is highly influenced by both the US and Japanese stock market in regime 1 with low-volatility, but only influenced by the Japanese stock market in regime 2 with high-volatility.
Suyi Kim; So-Yeun Kim; Kyungmee Choi. Modeling and analysis for stock return movements along with exchange rates and interest rates in Markov regime-switching models. Cluster Computing 2017, 22, 2039 -2048.
AMA StyleSuyi Kim, So-Yeun Kim, Kyungmee Choi. Modeling and analysis for stock return movements along with exchange rates and interest rates in Markov regime-switching models. Cluster Computing. 2017; 22 (S1):2039-2048.
Chicago/Turabian StyleSuyi Kim; So-Yeun Kim; Kyungmee Choi. 2017. "Modeling and analysis for stock return movements along with exchange rates and interest rates in Markov regime-switching models." Cluster Computing 22, no. S1: 2039-2048.
We apply the Hamilton 2-regime Markov Switching model to the stock returns along with exchange rates and interest rates from January 1993 to December 2016 in Korea. Two regimes are distinct in the Korean stock market. In regime 1 with low-volatility, the stock returns of Korea are significantly affected first by their exchange rates and secondly by their interest rates. More precisely, both exchange rates and interest rates negatively influence the stock returns during relatively stable periods in Korea. In regime 2 with high-volatility, the Korean stock market is explained by none of the two explanatory variables.
Suyi Kim; So-Yeun Kim; Kyungmee Choi. Markov Regime-Switching Models for Stock Returns Along with Exchange Rates and Interest Rates in Korea. Lecture Notes in Electrical Engineering 2017, 253 -259.
AMA StyleSuyi Kim, So-Yeun Kim, Kyungmee Choi. Markov Regime-Switching Models for Stock Returns Along with Exchange Rates and Interest Rates in Korea. Lecture Notes in Electrical Engineering. 2017; ():253-259.
Chicago/Turabian StyleSuyi Kim; So-Yeun Kim; Kyungmee Choi. 2017. "Markov Regime-Switching Models for Stock Returns Along with Exchange Rates and Interest Rates in Korea." Lecture Notes in Electrical Engineering , no. : 253-259.
The energy consumption of Korea’s manufacturing sector has sharply increased over the past 20 years. This paper decomposes the factors influencing energy consumption in this sector using the logarithmic mean Divisia index (LMDI) method and analyzes the specific characteristics of energy consumption from 1991 to 2011. The analysis reveals that the activity effect played a major role in increasing energy consumption. While the structure and intensity effects contributed to the reduction in energy consumption, the structure effect was greater than the intensity effect. Over the periods, the effects moved in opposite directions; that is, the structure effect decreased when the intensity effect increased and vice versa. The energy consumption by each industry is decomposed into two factors, activity and intensity effects. The increase of energy consumption due to the activity effect is largest in the petroleum and chemical industry, followed by the primary metal and non-ferrous industry, and the fabricated metal industry. The decrease of energy consumption due to the intensity effect is largest in the fabricated metal industry, followed by the primary metal and non-ferrous industry, and the non-metallic industry. The energy consumption due to intensity effect in the petroleum and chemical industry has risen. To save energy consumption more efficiently for addressing climate change in this sector, industrial restructuring and industry-specific energy saving policies should be introduced.
Suyi Kim. LMDI Decomposition Analysis of Energy Consumption in the Korean Manufacturing Sector. Sustainability 2017, 9, 202 .
AMA StyleSuyi Kim. LMDI Decomposition Analysis of Energy Consumption in the Korean Manufacturing Sector. Sustainability. 2017; 9 (2):202.
Chicago/Turabian StyleSuyi Kim. 2017. "LMDI Decomposition Analysis of Energy Consumption in the Korean Manufacturing Sector." Sustainability 9, no. 2: 202.
This paper analyzes the CO2 emissions from electricity generation in Korea during the period from 1990 to 2012 using both additive and multiplicative logarithmic mean Divisia index (LMDI) factor decomposition methods. We decompose these emissions into five factors: production effect, generation mix effect I due to the changes in the ratio of fossil-fueled electricity production to the total electricity production, generation mix effect II due to the changes in fossil-fuel mix, generation efficiency effect, and emission factor effect. The results show that most factors cause an increase in greenhouse gas emissions, although the extent varies. In particular, first, the increase in GHG emissions of this sector is predominantly influenced by the production effect. Second, the electricity generation structure has been developing in a direction opposite to that of greenhouse gas mitigation, and the fossil-fuel mix contributed to increasing the GHG emissions. Third, the electricity generation efficiency deteriorated toward an increase of GHG emissions. Fourth, the emission factor effect has contributed, as the only positive factor, to decreasing greenhouse gas emissions, but its influence has remained negligible. Taking these results together, we suggest a couple of energy and environmental policies to achieve the greenhouse gas mitigation target of electricity generation.
Suyi Kim; Sung-Kyun Kim. Decomposition analysis of the greenhouse gas emissions in Korea's electricity generation sector. Carbon Management 2016, 7, 249 -260.
AMA StyleSuyi Kim, Sung-Kyun Kim. Decomposition analysis of the greenhouse gas emissions in Korea's electricity generation sector. Carbon Management. 2016; 7 (5-6):249-260.
Chicago/Turabian StyleSuyi Kim; Sung-Kyun Kim. 2016. "Decomposition analysis of the greenhouse gas emissions in Korea's electricity generation sector." Carbon Management 7, no. 5-6: 249-260.
In this article, we decomposed Korean industrial manufacturing greenhouse gas (GHG) emissions using the log mean Divisia index (LMDI) method, both multiplicatively and additively. Changes in industrial CO2 emissions from 1991 to 2009 may be studied by quantifying the contributions from changes in five different factors: overall industrial activity (activity effect), industrial activity mix (structure effect), sectoral energy intensity (intensity effect), sectoral energy mix (energy-mix effect) and CO2 emission factors (emission-factor effect). The results indicate that the structure effect and intensity effect played roles in reducing GHG emissions, and the structure effect played a bigger role than the intensity effect. The energy-mix effect increased GHG emissions, and the emission-factor effect decreased GHG emissions. The time series analysis indicates that the GHG emission pattern was changed before and after the International Monetary Fund (IMF) regime in Korea. The structure effect and the intensity effect had contributed more in emission reductions after rather than before the IMF regime in Korea. The structure effect and intensity effect have been stimulated since the high oil price period after 2001.
Kyonghwa Jeong; Suyi Kim. LMDI decomposition analysis of greenhouse gas emissions in the Korean manufacturing sector. Energy Policy 2013, 62, 1245 -1253.
AMA StyleKyonghwa Jeong, Suyi Kim. LMDI decomposition analysis of greenhouse gas emissions in the Korean manufacturing sector. Energy Policy. 2013; 62 ():1245-1253.
Chicago/Turabian StyleKyonghwa Jeong; Suyi Kim. 2013. "LMDI decomposition analysis of greenhouse gas emissions in the Korean manufacturing sector." Energy Policy 62, no. : 1245-1253.