This paper measures and identifies the effects of urban form on travel behavior in Korea. The characteristics of urban form include urban size, density, distribution and clustering. Using cluster analysis, urban form in Korea is categorized into two groups: group 1 (i.e., large-sized, high-density, equally distributed and highly clustered areas) and group 2 (i.e., small-sized, low-density, unequally distributed and highly dispersed areas). The results showed that the large-sized, high-density, unequally distributed and dispersed pattern is a relevant strategy for both groups to minimize vehicle kilometers traveled (VKT) per capita. For group 1, increasing the average travel distance may be an efficient strategy to reduce the number of automobile trips. For group 2, however, decreasing the average travel distance may be a more efficient strategy. Previous recommendations for a so-called compact urban form require more validation before adoption in Korea. Different strategies are required for areas that show different characteristics in order to reduce VKT. It is important that planners and policy decision makers understand the relevant implications of urban form on travel behavior and energy use in order to implement spatial urban developments aimed at sustainability. JEL Classification
Abstract:Increasing precipitation by climate change and the growing number of impervious areas present greater risk of disaster damage in urban areas. Urban green infrastructure can be an effective mitigation alternative in highly developed and concentrated area. This study investigates the effect of various types of urban green infrastructure on mitigating disaster damage in Korea. Tobit model is used to analyze the factors that determine disaster damage. Damage variation is predicted with scenarios of RCP 8.5 and urban green spaces. Seventy-four districts and counties in seven metropolitan areas are defined as the unit and the period from 2005 to 2013 is considered in the analysis. The results indicate that higher urban green ratio, sewer length, financial independence rate, and local government's budget are relating to lower disaster damage. Based on a precipitation level of RCP 8.5 scenario in 2050, an increase in economic damage is expected to range from 262 to 1086%. However, with an increase in urban green ratio by 10%, increased economic damage is only expected to range from 217 to 1013%. The results suggest that green spaces play important role to mitigate precipitation related disasters. Highly concentrated urban areas need to consider various types of urban green infrastructure to prepare for an increase in precipitation due to climate change.
Fine particulate matter (PM2.5) is one of the main air pollution problems that occur in major cities around the world. A country’s PM2.5 can be affected not only by country factors but also by the neighboring country’s air quality factors. Therefore, forecasting PM2.5 requires collecting data from outside the country as well as from within which is necessary for policies and plans. The data set of many variables with a relatively small number of observations can cause a dimensionality problem and limit the performance of the deep learning model. This study used daily data for five years in predicting PM2.5 concentrations in eight Korean cities through deep learning models. PM2.5 data of China were collected and used as input variables to solve the dimensionality problem using principal components analysis (PCA). The deep learning models used were a recurrent neural network (RNN), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM). The performance of the models with and without PCA was compared using root-mean-square error (RMSE) and mean absolute error (MAE). As a result, the application of PCA in LSTM and BiLSTM, excluding the RNN, showed better performance: decreases of up to 16.6% and 33.3% in RMSE and MAE values. The results indicated that applying PCA in deep learning time series prediction can contribute to practical performance improvements, even with a small number of observations. It also provides a more accurate basis for the establishment of PM2.5 reduction policy in the country.
Farmland exhibits multifunctionality by preventing flooding and soil erosion and providing social and cultural community comfort. All these functions are essential for sustainable rural development. However, the multifunctionality of farmland is decreasing worldwide because of an aging society, depopulation and income disparity between flat lands and hilly mountainous lands. Regarding the consequences of abandonment, abandonment is intimately linked with the wider issue of the stagnation of the rural economy. The direct payment policy for hilly mountainous land is aimed at restraining farmland abandonment through community-based activities. The panel data difference in differences (DID) estimator was employed to observe the effect of direct payments on the rate of restraining farmland abandonment at the municipality level of the Hokkaido prefecture in Japan for the period of 2005–2015. We estimated that the direct payment implementation provided a 2% effectiveness for restraining the increase in the rate of abandonment as the result of DID estimation. On the other hand, the age group of 65 years or older was negatively correlated with farmland abandonment, which contradicts the general understanding. Older farmers have relatively more interest in contributing to and preserving their community. Therefore, the direct payment can encourage them to participate more in their community preservation. From these results, we concluded that it is necessary to promote farmland consolidation to compensate for the lack of inheritors. In addition, providing direct payment for a well-organized community or active stakeholders can be an effective way of utilizing governmental budgets and sustaining rural development.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.