논문 상세보기
Ecological restoration has become a vital strategy for tackling global environmental challenges. This study utilized BERTopic, a neural network-based topic modeling technique, to analyze research trends in ecological restoration technologies across 9,557 academic papers published between 1992 and 2024. Seven major research categories were identified: soil remediation, water quality improvement, habitat and biodiversity restoration, nature-based solutions, seed-based restoration, contaminated site restoration, and wildfire management. The compound annual growth rate analysis revealed that nature-based solutions had the highest growth rate, followed by soil remediation and seed-based restoration. Keyword co-occurrence network analysis identified three major clusters: Nature-based Solutions, Soil & Microbial, and Water Quality, with nature-based solutions acting as the central hub connecting various research themes. Country-level analysis indicated that China led global research output, followed by the USA, while South Korea ranked third, particularly excelling in soil remediation and water quality improvement. The iterative BERTopic approach effectively separated ecological restoration studies from biomedical research that shares similar terminology, addressing the limitations of traditional keyword-based analyses. These findings shed light on the evolution and regional characteristics of ecological restoration research, providing valuable insights for future research priorities and policy development.
1. 서 론
2. 재료 및 방법
2.1. 데이터 수집
2.2. BERTopic 분석
2.3. 토픽 분류 및 명명
2.4. 연구 동향 및 국가별 분석
2.5. 분석 및 시각화 도구
3. 결 과
3.1. 연도별 논문 발표 추이
3.2. 토픽 분류 결과
3.3. 연평균성장률 분석
3.4. 토픽 간 유사도 분석
3.5. 키워드 동시출현 네트워크 분석
3.6. 국가별 분석
3.7. 한국 연구 동향 분석
4. 고 찰
4.1. BERTopic 분석의 효용성
4.2. 주요 연구 주제의 특성
4.3. 연구 성장 동향
4.4. 한국 생태복원 연구의 현황과 과제
4.5. 연구의 한계 및 향후 방향
적 요
CRediT authorship contribution statement
Declaration of Competing Interest
ACKNOWLEDGEMENTS
REFERENCES
