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

在过去的十年中，水和能源供应模式的转变在全国和世界许多地方都出现了。已加紧努力将分散的和替代的水和能源系统，例如雨水收集、灰水回收和太阳能系统，纳入现有的集中网络（即电网、市政供水系统）。虽然这种整合可能会提高我们的水和能源供应对自然和人为安全威胁的抵御能力，但分散的系统往往缺乏规模经济，因此可能会带来更多的环境和社会经济成本，具体取决于技术和地理位置。如果没有对这样的集成进行仔细的计划和设计，并且没有足够的采用，它们可能会导致意想不到的后果，例如生产过剩、资源获取中的冲突，以及总体上更大的资源使用。规划和设计涉及从个人偏好/选择到水能系统关系的多个尺度的巨大复杂性。该项目运用计算机科学/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业知识，在国家关键需求领域开发这些复杂性的新知识。这项工作使决策者了解在不同的分散式水和能源采用方案中可能产生的结果和权衡。该项目促进了分散系统的规划和设计，并为政策制定提供信息，以便为城市社区创建更具可持续性（对环境影响更小）和弹性（能够从中断中恢复）的基础设施系统。该项目旨在发展对未来人口、气候和技术情景下集中式和分散式水和能源系统集成背后复杂性的理解和认识，以追求弹性和可持续性。这项研究使用调查工具来描述与水和能源基础设施系统（去）中心化有关的个人偏好（效用函数）；一个众包平台，用于有效的利益相关者参与和响应收集；一个基于空间主体的模型，根据主要大都市案例研究地点的效用函数和特征，开发空间上明确的收养轨迹和模式；一个系统动力学模型，考虑基础设施系统之间的相互作用，表征弹性和可持续性的措施，并将这些反馈给基于代理的模型；以及一个跨尺度空间优化模型，以理解和描述可能的最佳情况结果，并为政策和激励/抑制计划的设计提供信息。综合起来，这些方法提供了一种强大的能力，可以考虑未来能源和水资源的开发可以或多或少具有弹性，对环境的影响可以少或多，满足人口的不同需求，并导致或多或少的总体资源使用。波士顿和亚特兰大是通过这个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.