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SPECIAL THEME 1_화재원인조사의 동향과 발전방향 - 미국의 민관 화재원인조사 시스템 구축현황

Schaal, Robert J.
- 방재와보험
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- s.137
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- pp.7-10
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- 2010
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Worldwide Trend and Korean Recent Status in the Supply-Demand for Resources -The Current Situation of Recycling Technology for Waste Resources in Korea(1)- (자원수급(資源需給)의 세계적(世界的)인 추세(趨勢)와 우리나라의 동향(動向) -국내자원(國內資源)의 유효이용(有效利用)을 위한 처리(處理) 및 회수기술(回收技術) 동향조사(動向調査)(1)-)

Oh, Jae-Hyun;Kim, Mi-Sung;Cho, Sung-Baek
- Resources Recycling
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- v.15 no.3 s.71
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- pp.3-19
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- 2006
Sustainable supply of minerals and energy is global problem. Metals and energy consumption in the world has increased with economic growth. Currently more than 40 metals are systematically extracted and used in many different fields in civilized society. Recycling and reuse have become very important because recycling contributes to supplying the materials and protecting the environment of society. It is not realistic that all waste materials are capable of being recycled, because recycling metals have fundamentally been competing with primary production. In this point of view, prior to discuss on current recycling technology of waste resources in Korea, world wide trend and Korean recent activity in the supply-demand far minerals and energy resources are reviewed.
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A study on time series linkage in the Household Income and Expenditure Survey (가계동향조사 지출부문 시계열 연계 방안에 관한 연구)

Kim, Sihyeon;Seong, Byeongchan;Choi, Young-Geun;Yeo, In-kwon
- The Korean Journal of Applied Statistics
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- v.35 no.4
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- pp.553-568
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- 2022
The Household Income and Expenditure Survey is a representative survey of Statistics Korea, which aims to measure and analyze national income and consumption levels and their changes by understanding the current state of household balances. Recently, the disconnection problem in these time series caused by the large-scale reorganization of the survey methods in 2017 and 2019 has become an issue. In this study, we model the characteristics of the time series in the Household Income and Expenditure Survey up to 2016, and use the modeling to compute forecasts for linking the expenditures in 2017 and 2018. In order to evenly reflect the characteristics across all expenditure item series and to reduce the impact of a specific forecast model, we synthesize a total of 8 models such as regression models, time series models, and machine learning techniques. In particular, the noteworthy aspect of this study is that it improves the forecast by using the optimal combination technique that can exactly reflect the hierarchical structure of the Household Income and Expenditure Survey without loss of information as in the top-down or bottom-up methods. As a result of applying the proposed method to forecast expenditure series from 2017 to 2019, it contributed to the recovery of time series linkage and improved the forecast. In addition, it was confirmed that the hierarchical time series forecasts by the optimal combination method make linkage results closer to the actual survey series.
https://doi.org/10.5351/KJAS.2022.35.4.553 인용 PDF KSCI