CNH-L: People, Place, and Payments in Complex Human-Environment Systems

该项目旨在促进对生态系统服务付费的影响的理解，这是一种全球保护方法，旨在激励基本自然资源的使用者保护相关生态系统。近几十年来，这些方案越来越受欢迎，越来越多的并行生态系统服务费用支付方案已被部署，以减轻环境退化和过度开发自然资源。生态系统服务费用支付方案往往是在同一时间和空间背景下同时实施的，但对这种相互作用、并行的生态系统服务费用支付方案的机制、途径以及社会和生态影响却知之甚少。这个项目将分析如何以及为什么并行PES程序相互作用，并与相应的耦合自然和人类系统。这项工作将导致对潜在的、往往是无意的社会和生态后果的深入了解。生态系统服务补偿方案可能会影响人口迁移、教育投资以及当地家庭与自然环境之间的其他联系。此外，该项目还将解决这些项目如何影响土地覆盖、土地利用和物种栖息地随时间的变化。该项目将通过解决发展保护政策与政府在生态系统服务补偿方案中投资的有效性之间的相互作用来造福社会。该项目的广泛影响还扩展到指导K-12学生及其教师的创新教育和推广活动，创造性地使用新的地理空间技术有效监测和保护濒危野生动物，开发以学生为中心的教学法，提高对复杂的人类环境系统的理解，以及开发相关电影和基于网络的PES中心，以传播项目成果。 这项研究将培养研究生，促进美国和中国科学家之间的合作。 该项目旨在了解并行生态系统服务补偿方案之间的相互关系，揭示这些方案在空间和时间上的相关社会和生态后果。PI将回答以下问题：（1）通过什么途径或中介变量做并行PES程序，通过他们对人类和自然子系统的影响，相互影响？(2)并行的生态系统服务费用支付方案在哪里以及在多大程度上导致环境的净变化？(3)在这些干预措施下，人类-环境系统在空间和时间上如何演变？在人的子系统内，方案执行指标将侧重于移徙和就业机会的影响，以及参与家庭的教育投资。在自然子系统中，PI将记录和监测土地覆盖和土地使用变化，以及使用无人驾驶飞行器，相机捕获和卫星成像技术的主要哺乳动物物种的占用和栖息地。最后，PI将开发一个复杂的系统框架，其中包括地理空间和统计分析，结构方程建模，以及开发一个基于代理的模型（ABM），将各种空间和时间尺度的定性和定量数据和模型集成在一起。虽然该项目将使用来自中国梵净山国家级自然保护区（FNNR）的数据，但该研究将为理解许多耦合自然和人类系统中并行PES计划背后的机制提供见解和方法，大大提高科学，技术，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准。

This project seeks to advance the understanding of the impact of payments for ecosystem services (PES), a global conservation approach that incentivizes users of essential natural resources to protect the related ecosystems. In recent decades these programs have increased in popularity, and an increasing number of parallel PES programs have been deployed to mitigate environmental degradation and the over-exploitation of natural resources. PES programs are often implemented concurrently in the same temporal and spatial context, yet little understanding exists about the mechanisms, pathways, and social and ecological effects of such interacting, parallel PES programs. This project will analyze how and why parallel PES programs interact with one another and with the corresponding coupled natural and human system. This work will result in generating insight into the potential, often unintended, social and ecological consequences. PES programs may impact human migration, investment in education, and other connections between local households and the physical environment. Furthermore, the project will address how such programs may affect changes in land cover, land use, and species habitat over time. This project will benefit society by addressing the interactions between development conservation policy and the effectiveness of governmental investments made in PES programs. This project's broader impacts also extend to innovative education and outreach activities that mentor K-12 students and their teachers, creative use of new geospatial technologies for effective monitoring and conservation of endangered wildlife, development of a student-centered pedagogy that enhances understanding of complex human-environment systems, and the development of a related movie and a web-based PES Center to disseminate project results. The research will train graduate students and foster collaboration between U.S. and Chinese scientists. The project aims to understand the reciprocal relationship between parallel PES programs, shedding light on the related social and ecological consequences of these programs over space and time. The PIs will answer the following questions: (1) Through what pathways or mediating variables do parallel PES programs, through their influence on human and natural subsystems, affect one another? (2) Where and to what extent do parallel PES programs lead to net changes in the environment? (3) How has and will the human-environment system evolve over space and time given these interventions? Within the human subsystem the PIs will focus on the impacts of migration and job opportunities, as well as the educational investments of participating households. Within the natural subsystem, the PIs will document and monitor land cover and land use change, as well as occupancy and habitat of major mammal species using an unmanned aerial vehicle, camera trapping, and satellite imaging techniques. Lastly, the PIs will develop a complex systems framework, which consists of geospatial and statistical analyses, structural equation modeling, and development of an agent-based model (ABM) integrating qualitative and quantitative data and models across various spatial and temporal scales. Although this project will use data from Fanjingshan National Nature Reserve (FNNR) in China, the research will provide insights and approaches for understanding the mechanisms behind parallel PES programs in many coupled natural and human systems, substantially improving the science, technology, and practical effectiveness of PES.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.