2021, Vol.109, No.

The Korea Spatial Planning Review 2023 KCI Impact Factor : 1.2

pISSN : 1229-8638
https://journal.kci.go.kr/krihs

In this study, a water footprint methodology that reflects the characteristics of domestic water resources, was developed and calculated the water footprint for expressway system as an infrastructure. Water footprint characterization factors were developed for groundwater and surface water in six representative watersheds which are Han River Basin, Nakdong River Basin, Geum River Basin, Seomjin River Basin, Youngsan River Basin, and Jejudo. Three different water footprint methodologies, Boulay’s methodology, Korea Ministry of Environment methodology, this study methodology were compared in an actual construction case of expressway. As the results, the water footprint of 1 km and 4 lane expressway with Boulay and MOE methodology showed 58,804 ㎥ H2Oeq, 42,036 ㎥ H2Oeq, respectively throughout whole life cycle. However, 9,478 ㎥ H2Oeq. of water footprint was calculated by using this study methodology. This difference is mainly due to a developed regional characterization factor, and its related water consumption coefficient of the construction materials.

The heat wave, which was first designated as a natural disaster in 2018 and causes casualties and property damage due to temperature rises above a certain standard, is becoming more serious at domestic and abroad. In particular, Busan faces the need to establish strategies to cope with the heat wave for its largest number of thermal disease among all metropolitan areas of the Korean Peninsula. This study aims to provide foundations for establishing heat wave strategies by using LSTM techniques, an artificial intelligence (AI) methodology, to reflect the urban-social characteristics of Busan. LSTM optimization analysis results show higher accuracy than conventional regression models and ensemble models, identified by MAE 0,139 and MSE 0.128. In addition, we perform a feature importance analysis to examine the effects of the utilized variables, and the results showed that the temperature-related variables had the highest impact. Significance of this study is found in predicting heat waves by reflecting the urban-social characteristics of Busan beyond simply utilizing climate data through AI methodology. It is expected that heat waves would be more accurately predicted by supplementing future data, adding variables, and improving models.

This study analyzed the geographic proximity between generations to support the generation-friendly housing support policy that solves the social problems of the low birth rate and aging age. A fixed-effect panel logit model has been developed with 2nd~6th KLoSA data for elderly parent households and adult children’s households. Analysis results indicate that common effects are as follows. The geographic proximity between generations is high during the physical decline period in the life cycle of elderly parents and rearing period in the family life cycle of adult children. The geographic proximity between generations is low when living with children in the elderly parent household. On the other hand, the discriminatory effects in urban areas were the economic constraints of elderly parents and the time constraints of adult children. Accordingly, the implication are as follows. First, it is confirmed that overseas studies findings are also applied in korea. Second, it is judged that the family function is performed despite of nuclear family society. Third, it can be used as basic data for the generation-friendly housing support policy plan.