• 제목/요약/키워드: Subnational Government

검색결과 4건 처리시간 0.017초

How Did South Korean Governments Respond during 2015 MERS Outbreak?: Application of the Adaptive Governance Framework

  • Kim, KyungWoo
    • Journal of Contemporary Eastern Asia
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    • 제16권1호
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    • pp.69-81
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    • 2017
  • This study examines how South Korean governments responded to the outbreak of Middle East Respiratory Syndrome Coronavirus (MERS) using the adaptive governance framework. As of November 24, 2015, the MERS outbreak in South Korea resulted in the quarantine of about 17,000 people, 186 cases confirmed, and a death of 38. Although the national government had overall responsibility for MERS response, there is no clear understanding of how the ministries, agencies, and subnational governments take an adaptive response to the public health crisis. The paper uses the adaptive governance framework to understand how South Korean governments respond to the unexpected event regarding the following aspects: responsiveness, public learning, scientific learning, and representativeness of the decision mechanisms. The framework helps understand how joint efforts of the national and subnational governments were coordinated to the unexpected conditions. The study highlights the importance of adaptive governance for an effective response to a public-health related extreme event.

China's Debt Woes: Not Yet a "Lehman Moment"

  • Sharma, Shalendra D.
    • East Asian Economic Review
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    • 제19권1호
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    • pp.99-114
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    • 2015
  • What explains the sharp increase in the Chinese economy's indebtedness, in particular the China's onshore corporate debt? Has the overall debt burden reached a threshold where it poses a systemic risk, thereby making the economy vulnerable to a "Lehman Moment" - with disorderly unwinding of the private sector and sovereign debt? What are the short and longer term implications of China's growing debt problems on domestic economic growth and the broader global political economy? What has Beijing done to ameliorate the problem, how effective were its efforts, and what must it do to deal with this problem?

Reconsideration of the Public Diplomacy Act in Korea and a Few Suggestions

  • Park, Jongho;Kim, Ho
    • International Journal of Advanced Culture Technology
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    • 제10권2호
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    • pp.154-161
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    • 2022
  • The Korean government has recently invigorated the activities of public diplomacy. It is based on the Public Diplomacy Act enacted in 2016. However, there is a widespread concern that it was belatedly enacted and showed necessity to a revision. We believe that this paper contains three contributions which were not sufficiently addressed before. First, we identify the current state of public diplomacy-related legislation in Korea. Second, we argue the necessity to critically review the legal adequacy of Public Diplomacy Act with a consideration of rapidly changing external environment. Lastly, we propose several ways of revision for the future development of public diplomacy in Korea. When revising the Act, it is necessary to make clear a legal connection between the general law and the special law as in the case of the Korea Foundation Act and the Public Diplomacy Act. In this regard, it is worth examining the relationship between the Framework Act on International Development Cooperation and related norms. In addition, the role of the private sector and subnational governments should be expanded. For this purpose, a method and level of cooperation with the private sector should be clearly defined.

시계열 위성영상과 머신러닝 기법을 이용한 산림 바이오매스 및 배출기준선 추정 (Machine-learning Approaches with Multi-temporal Remotely Sensed Data for Estimation of Forest Biomass and Forest Reference Emission Levels)

  • 이용규;이정수
    • 한국산림과학회지
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    • 제111권4호
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    • pp.603-612
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    • 2022
  • 본 연구는 다중시기 위성영상과 머신러닝 알고리즘을 이용하여 준국가수준의 시계열 산림바이오매스량을 추정하였으며, 이를 바탕으로 산림배출기준선 설정하여 비교·분석하였다. 머신러닝기반의 산림바이오매스 추정 모델을 구축하기 위하여 Landsat TM 위성영상과 유럽항공우주국에서 제공하는 Biomass Climate Change Initiative 정보를 이용하였으며, 머신러닝 알고리즘은 비모수 학습모델인 k-Nearest Neighbor(kNN)과 의사결정나무 기반의 Random Forest(RF)를 적용하였다. 또한, 추정된 산림바이오매스량은 Forest reference emission levels(FREL) 자료와 비교하였다. 머신러닝 알고리즘 별 산림바이오매스 추정 모델을 비교해보면, 최적의 kNN 모델과 RF 모델의 Root Mean Square Error (RMSE)는 각각 35.9와 34.41였으며, RF모델이 kNN모델보다 상대적으로 우수하였다. 또한, FREL, kNN, RF 모델 별 산림배출기준선의 기울기는 각각 약 -33천ton, -253천ton, -92천ton으로 설정되었다.