Hyungho Kim

Analysis of the effect of climate change on site index of Korean white pine

This study estimated a site index model for Pinus koraiensis (Korean White Pine) based on the weighted least squares (WLS) model and to examine the effects of climate change on it. The site index was chosen as the dependent variable and site, weather, and edaphic factors were chosen as independent variables. Simulations were performed under three A1B climate change scenarios of the Intergovernmental Panel on Climate Change, with the temperature ranging from −3.3 °C to +3.3 °C. The simulation results showed that the site index decreased, peaking at −1.3 °C. The decreased level of the site index by region was also analyzed. Scenario 1 and Scenario 2 showed the increases in the site index in 7.3% and 2.8% of the total area respectively. However, in Scenario 3, no additional increase in the site index was observed, and the degree of the decrease was also broader and more diverse than for the other two scenarios.

The Effects of Spatial and Climatic Factors on Woody Plant Richness along an Extensive Altitudinal Gradient in South Korea

This study was conducted to clarify the richness patterns of woody plants along a temperate altitudinal gradient on the Baekdudaegan ridge, South Korea. The effects of the spatial and climatic factors on the observed altitudinal richness patterns were evaluated. We also tested Rapoport’s altitudinal effect, which relates the distribution of a species’ altitudinal ranges to the patterns in species richness. Woody plant data were collected from 1100 plots on the Baekdudaegan ridge. A total of 248 woody plant species from 47 families and 99 genera were found. The altitudinal pattern of the woody plant species richness on the Baekdudaegan ridge exhibited a clear hump-shaped pattern with a peak around 800 m. Spatial factors(mid-domain effect and area) were the primary drivers in the simple linear models, whereas the climatic factors(mean annual precipitation and temperature) had lower explanatory power. Multiple linear regression analysis revealed that the combined interaction between spatial and climatic factors affected the altitudinal richness patterns of the overall woody plants. Furthermore, the spatial and climatic factors were more important for large- and small-ranged woody plants, respectively. The results of Stevens’ method and the midpoint method do not support Rapoport’s altitudinal effect. The results suggest that a combined interaction between spatial and climatic factors influences the richness pattern of the total woody plant species. Furthermore, the relative importance of these factors depends on the range size of the woody plants species along an altitudinal gradient on the ridge of the Baekdudaegan ridge.

Policy Suggestion as the Analysis of Bio-ethanol Demand

As a result of bio-ethanol demand function estimation on time series data to estimate economic impact of bio-ethanol, the statistically significant model is estimated. In the demand model demand elasticity of bio-ethanol shows elasticity, and it is represented with incomplete substitute relation to gasoline in terms of energy use. In terms of bio-ethanol quantity on the price of bio-ethanol scenario in change based on the demand function, when the price increases with the elasticity of bio-ethanol price, relatively substantial decrease of demand quantity, the demand quantity of bio-ethanol increase with substitute goods increase of gasoline price.