Doctoral Dissertation Research: Computational Modeling of the Effects of Climate Change and Land Acquisition on Household Dynamics in Southern Ethiopia

目前发展中国家大规模征地的激增是一个复杂的社会-自然系统问题，也正在成为一个全球性的政策问题。大量土地被租给设在贫穷国家和工业化国家的农业综合企业。由此导致的大规模征地扩大，通过创造就业机会、发展基础设施和转让技术，对农村经济做出了贡献。然而，它也对农村家庭产生负面影响，因为大多数大规模征地（加上气候变化的持续影响）限制了农村人口获得资源的机会，从而影响了他们的游牧生计，最终导致他们被剥夺和流离失所。尽管基于主体的模型以前已被用于模拟人类-环境系统的复杂性，但大型行动者的影响及其与当地家庭的相互作用尚未得到解决，并且仍然知之甚少。本博士论文研究项目将应用基于主体的建模仿真方法，明确表示主要参与者（企业、家庭和机构），以了解他们与埃塞俄比亚西南部南奥莫地区环境的相互作用和动态。该博士生将整合基于空间主体的建模和地理信息系统的方法，以及社会网络分析和定量分析。智能体仿真模型将使用定量和定性的社会经济数据，并基于地理数据进行明确的空间参考，从而使智能体能够在类似于现实世界的环境中进行交互。他还将使用参与式建模策略，包括在建模的每个阶段之前、期间和之后与当地利益相关者（如农业综合企业、政府官员和当地社区家庭）进行协商。该项目将增进对农村家庭与企业之间动态互动的了解；行动者在土地上不同行动的后果和相互反馈；以及气候变化对生态系统和当地社区的影响。通过使用复杂系统方法，该项目将有助于在多个时空尺度上对人类和环境成分的非线性动态相互作用有新的认识。该项目将通过明确建模异质参与者、机构及其环境的动态，增强当前关于人类环境系统的科学知识。参与式建模方法将使广泛的选区接触到复杂社会环境系统的科学研究。该项目还将为关于这一重大发展问题的科学政策辩论提供新的、有科学依据的观点。作为博士论文研究改进奖，该奖项还将提供支持，使有前途的学生建立一个强大的独立研究生涯。

The current surge in large-scale land acquisition in developing countries is a complex socio-natural systems problem that also is emerging as a global policy issue. Significant tracts of land are being leased to agribusiness enterprises based in both poor and industrialized countries. The resulting expansion of large-scale land acquisition contributes to the rural economy by creating jobs, developing infrastructure, and transferring technologies. It also negatively affects rural households, however, because most large-scale land acquisition (combined with ongoing effects of climate change) affects the pastoralist livelihoods of rural people by limiting their access to resources, eventually leading them to dispossession and displacement. Although agent-based models previously have been used for modeling complexity in human-environmental systems, the influence of large actors and their interactions with local households have not been addressed and remain poorly understood. This doctoral dissertation research project will apply an agent-based modeling simulation approach that explicitly represents the main actors (enterprises, households, and institutions) to understand their interactions and dynamics with the environment in the South Omo region in southwestern Ethiopia. The doctoral student will integrate methods from spatial agent-based modeling and GIS along with social network analysis and quantitative analysis. The agent simulation model will use quantitative and qualitative socioeconomic data, with explicit spatial referencing based on geographic data, thereby enabling agents to interact in environments resembling the real world. He also will use a participatory modeling strategy that involves consultation before, during, and after each stage of modeling with local stakeholders, such as agribusiness enterprises, government officials, and local community households.The project will enhance understanding regarding dynamic interactions between rural households and enterprises; the consequences of different actions of actors on the land and reciprocal feedbacks; and the impact of climate change on ecosystems and local communities. By using a complex system approach, the project will contribute fundamental new understanding regarding the nonlinear dynamic interaction of human and environment components at multiple spatial and temporal scales. The project will enhance current scientific knowledge about human-environment systems by explicitly modeling the dynamics of heterogeneous actors, institutions, and their environments. The participatory modeling approach will expose a broad spectrum of constituencies to scientific research on complex socio-environmental systems. The project also will provide new, scientifically informed perspectives for the science-policy debate on this significant development issue. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.