텍스트 분석을 활용한 기후변화 연구 동향 분석 - 한국기후변화학회지를 중심으로 -

Abstract

Since the 1980s, studies on climate change have been increasing in various disciplines. To investigate systematically the research trend of climate change studies, this study conducted text network analysis and LDA (Latent Dirichlet Allocation) topic modeling of 1,388 keywords from 313 papers published in Journal of Climate Change in Korea. Based on degree centrality and betweenness centrality, the keyword ʹGHG Emissionsʹ attracted the attention from researchers the most, and ʹmitigationʹ was mentioned or studied together with other keywords. As a result of LDA, the studies were categorized into the six topics of 1) greenhouse gas emissions modeling, 2) greenhouse gas emission factors, 3) LULUCF(Land Use, Land‐Use Change, and Forestry) or CCS(Carbon Capture and Storage), 4) adaptation or mitigation measures and actors, 5) mitigation modeling or adaptation, and 6) inventory. The temporal configuration of topics shows the associated trends. This study found that all the topics have been studied without significant temporal differences in contrast to other disciplines, which can be attributed to the multidisciplinary characteristics of climate change.