摘要
Abstract
Climate change has brought significant impacts to the Qinghai Tibet Plateau,and has also had an impact on the livelihoods of local farmers and herdsmen.Studying the impact of local climate change and herdsmen's response strategies is not only the starting point for improving climate change adaptation capacity,but also an important support for establishing a regional pattern for adapting to climate change.Based on two field investigations of Z village in 2020 and 2023,and through questionnaire surveys and in-depth interviews with herders,this study analyzes the effectiveness and challenges of local climate change adaptation according to the three-layer characteristics of adaptive governance.Research has found that in the face of changes in precipitation and temperature,lake expansion,vegetation changes,and disaster impacts,herdsmen actively adopt various methods to reduce the impact of climate change,demonstrating high adaptability and resilience.The successful experiences of these adaptation actions stem from"inner harmony",including cooperation between people and the harmonious rhythm between humans and nature.However,the lack of external support is still a challenge,as the case study lacks necessary external support,including not only direct disaster relief,but also protection of its adaptability and sustainability,as well as response to potential grassland ecological changes.If the external application continues to be sluggish,the existing adaptability will eventually be exhausted.It is proposed that the establishment of a regional pattern for adapting to climate change should follow the idea of multi center governance,unite multiple stakeholders,summarize successful experiences in climate change response in different regions,and form a fusion of multiple knowledge in order to form effective"external responses"to local areas and improve China's climate change adaptation capacity.关键词
气候变化/适应性治理/多中心治理/区域格局/雪灾Key words
climate change/adaptive governance/multi center governance/regional pattern/snow disaster分类
社会科学
