scholarly journals Positive or negative? Public perceptions of nuclear energy in South Korea: Evidence from Big Data

2019 ◽  
Vol 51 (2) ◽  
pp. 626-630 ◽  
Author(s):  
Eunil Park
Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5681
Author(s):  
Eunjung Lim

South Korea and Japan are two large contributors to global greenhouse gas emissions. In October 2020, President Moon Jae-in and Prime Minister Suga Yoshihide declared that their countries would aim for carbon neutrality by 2050. The Moon administration presented the Korean version of the New Deal that includes its Green New Deal, whereas the Suga administration completed its strategy aiming for green growth. Both countries emphasize the importance of energy transition through the expansion of green energy in power generation. However, they show some significant differences in dealing with nuclear energy. The purpose of this article is to compare the two countries’ energy policies and analyze the rationales and political dynamics behind their different approaches to nuclear energy. The study reveals that the contrast between the two political systems has resulted in differences between their policies. This study depends on comparative methods that use primary sources, such as governmental documents and reports by local news media.


2003 ◽  
Vol 807 ◽  
Author(s):  
Marko M. Ninkovic ◽  
Jagos J. Raicevic

ABSTRACTOne of the greatest challenges in the use of nuclear energy is the high radioactive long-lived waste which is generated during production. It must be dealt with safely and effectively. While technical solutions exist, including deep geological repositories, progress in the disposal of radioactive waste has been influenced, and in many cases delayed, by public perceptions about the safety of the technology. One of the primary reasons for this is the long life of many of radionuclides, actinides and fission products, with half-lives on the order of a hundred thousand to a millions years. Problems of perceptions could be reduced significantly, according to our and many others author's opinion, if there were a way to burn or destroy the most toxic long-lived radioactive wastes. As there are no industrial methods for waste destroying today, in this paper it was suggested a new hybrid, deterministic approach: instead of final waste disposal, long-termed but yet temporal storage only, striving towards final destruction once the appropriate conditions are maintained. This new or modified old approach could affect current HLLLW management and related activities in: changes of processing technology; prolonging the time period of waste storage at temporal depositories; increasing the investment into research regarding the methods and technologies for destructions of these materials, and slowing down the investments into the very expensive final disposal repositories. It is authors' opinion that such deterministic, conceptual approach would contribute the reviving interest in nuclear energy, all over the world and especially in small and developing countries.


Energy Policy ◽  
2018 ◽  
Vol 120 ◽  
pp. 436-447 ◽  
Author(s):  
Shirley S. Ho ◽  
Jiemin Looi ◽  
Agnes S.F. Chuah ◽  
Alisius D. Leong ◽  
Natalie Pang

A sentiment analysis using SNS data can confirm various people’s thoughts. Thus an analysis using SNS can predict social problems and more accurately identify the complex causes of the problem. In addition, big data technology can identify SNS information that is generated in real time, allowing a wide range of people’s opinions to be understood without losing time. It can supplement traditional opinion surveys. The incumbent government mainly uses SNS to promote its policies. However, measures are needed to actively reflect SNS in the process of carrying out the policy. Therefore this paper developed a sentiment classifier that can identify public feelings on SNS about climate change. To that end, based on a dictionary formulated on the theme of climate change, we collected climate change SNS data for learning and tagged seven sentiments. Using training data, the sentiment classifier models were developed using machine learning models. The analysis showed that the Bi-LSTM model had the best performance than shallow models. It showed the highest accuracy (85.10%) in the seven sentiments classified, outperforming traditional machine learning (Naive Bayes and SVM) by approximately 34.53%p, and 7.14%p respectively. These findings substantiate the applicability of the proposed Bi-LSTM-based sentiment classifier to the analysis of sentiments relevant to diverse climate change issues.


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