인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Understanding the interaction between ecosystem services (ESs) and tourism urbanization (TU) is essential for regional development and decision-making in urban agglomerations in the Yellow River Basin (YRB). Evaluating the coupling coordination degree (CCD) between ESs and TU is vital for regional development in the YRB. This study employs multi-year statistical data and spatial datasets-including land use, digital elevation models, meteorological data, and soil property data-to assess tourism urbanization using the entropy method. Concurrently, ESs, which encompass water yield, soil conservation, habitat quality, and carbon sequestration, are evaluated through the InVEST model. A spatiotemporal analysis of the YRB from 2000 to 2020 reveals two key trends: (1) the spatial polarization of tourism urbanization has intensified, with 68% of high-intensity clusters concentrated in eastern provinces (e.g., Shandong, Henan), sharply contrasting with the underdeveloped western regions (e.g., Qinghai, Ningxia); and (2) the coupling coordination degrees between TU and ESs have declined by 22-35% in ecologically fragile zones, driven by habitat fragmentation and carbon loss. Specifically, water yield and soil conservation have increased by 18% and 24%, respectively, while habitat quality and carbon sequestration have decreased by 14% and 11% in urbanizing areas, reflecting unsustainable trade-offs. The novel contribution of this study lies in establishing a basin-scale CCD framework for urban agglomerations, providing empirical evidence to reconcile tourism-driven growth with ecosystem resilience in the YRB. These findings underscore the urgent need for spatially adaptive governance to mitigate developmental imbalances. Future research should integrate high-resolution data and cultural ESs to address micro-scale complexities.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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