CRISP Type 1/Collaborative Research: Sustainable and Resilient Design of Interdependent Water and Energy Systems at the Infrastructure-Human-Resource Nexus

在过去的十年里，水和能源供应模式的转变已经出现在全国和世界的许多地方。已作出越来越多的努力，将分散的和替代的水和能源系统，如雨水收集、灰水回收和太阳能系统，纳入现有的中央网络(即电网、市政供水系统)。虽然这种整合可能会增加我们的水和能源供应对自然和人为安全威胁的复原力，但分散的系统往往缺乏规模经济，因此可能会增加环境和社会经济成本，这取决于技术和地理位置。如果不认真规划和设计此类整合并充分采用，它们可能会导致意想不到的后果，如生产过剩、资源获取中的冲突以及总体上更多地利用资源。从个人喜好/选择到水能源系统的关系，规划和设计在多个尺度上都涉及极大的复杂性。该项目应用了计算机科学/计算可持续性、经济学、基础设施系统分析和生命周期评估等领域的专门知识，以便在国家迫切需要的领域发展对这些复杂性的新知识。这项工作向决策者通报了在不同分散的水和能源采用情况下的可能结果和权衡。该项目促进了分散系统的规划和设计，并为政策制定提供了信息，以便为城市社区创建更可持续(较低环境影响)和更具弹性(能够从中断中恢复)的基础设施系统。该项目旨在加深对未来人口、气候和技术情景下集中式和分散式水和能源系统整合背后的复杂性的理解和了解，以追求复原力和可持续性。本研究使用调查工具来描述与水和能源基础设施系统(非集中化)相关的个人偏好(效用函数)；用于及时有效地参与利益相关者并收集响应的众包平台；基于主体的空间模型，以根据效用功能和主要大都市案例研究地点的特征制定空间上明确的采用轨迹和模式；系统动力学模型，考虑基础设施系统之间的相互作用，表征复原力和可持续性的测量，并将这些反馈给基于主体的模型；以及跨尺度空间优化模型，以了解和表征可能的最佳情况结果，并为政策和激励/抑制方案的设计提供信息。这些方法结合在一起，提供了一种强大的能力，可以考虑未来能源和水资源的发展可能或多或少具有弹性、产生或减少或更大的环境后果、满足人类人口的不同需求以及导致更多或更少的总体资源使用的方式。波士顿和亚特兰大是通过该项目开发的建模框架的试验台。

During the last decade, a transition in the water and energy supply paradigm has emerged in many places across the nation and the world. Increasing efforts have been made to integrate decentralized and alternative water and energy systems, such as rainwater collection, greywater recycling, and solar energy systems, into the existing centralized networks (i.e. electrical grid, municipal water supply system). While such integrations could potentially increase the resilience of our water and energy supplies to natural and man-made security threats, decentralized systems often lack economies of scale and hence could present increased environmental and socioeconomic costs depending on technologies and geographic locations. Without careful planning and design of such integrations and enough adoption, they could cause unintended consequences such as over-production, conflicts in resource acquisition, and an overall greater use of resources. Planning and design involves great complexities at multiple scales from individual preferences/choices to water energy systems nexus. This project applies expertise in the areas of computer science/computational sustainability, economics, infrastructure systems analysis, and life cycle assessment in a manner that develops new knowledge of these complexities in an area of critical national need. The work informs decision makers about possible outcomes and tradeoffs in different decentralized water and energy adoption scenarios. The project facilitates the planning and design of decentralized systems, and informs policy development to create more sustainable (lower environmental impacts) and resilient (able to recover from disruption) infrastructure systems for urban communities. This project aims to develop understanding and knowledge of complexities behind the integration of centralized and decentralized water and energy systems under future demographic, climate, and technology scenarios in pursuit of resilience and sustainability. This research uses survey instruments to characterize individual preferences (utility functions) related to (de)centralization of water and energy infrastructure systems; a crowdsourcing platform for time-effective stakeholder engagement and response collection; a spatial agent-based model to develop spatially explicit adoption trajectories and patterns in accordance with utility functions and characteristics of the major metropolitan case study locations; a system dynamics model that considers interactions among infrastructure systems, characterizes measures of resilience and sustainability, and feeds these back to the agent based model; and a cross-scale spatial optimization model to understand and characterize the possible best-case outcomes and to inform design of policies and incentive/disincentive programs. Combined, these methods provide a robust capacity to consider the ways in which future development of energy and water resources can be more or less resilient, have fewer or greater environmental consequences, meet differential demands of human populations, and result in greater or lesser overall resource use. Boston and Atlanta are the testbeds for the modeling framework developed through this project.