CAREER: Understanding the Fundamental Principles Driving Household Energy and Resource Consumption for Smart, Sustainable, and Resilient Communities

最终，美国人消耗的能源和资源的数量在很大程度上取决于他们在哪里以及如何生活。因此，北方各州的居民往往比南方各州的居民在家里消耗更多的能源，因为供暖比制冷需要更多的能源。同样，住在郊区的人往往比住在市中心的人在交通上消耗更多的能源，因为通勤时间更长会消耗更多的能源。类似的观察结果也适用于所有类型的能源和资源，从电力和运输到天然气、水，甚至食物。这项教师早期职业发展（Career）资助将有助于发现控制人们生活在哪里和“如何”对能源和资源消耗有多大影响的基本原则。这项研究的贡献将直接有助于制定有效的政策，促进消耗更少能源和资源的更可持续的社区。此外，更好地了解城市的能源流动将为规划者和工程师提供信息，使他们能够设计更智能、更有弹性的分散和分布式基础设施系统。此外，这笔拨款将对更广泛的公众产生重大影响，因为它包括开发一款智能手机应用程序，使任何人都能计算自己的日常碳足迹，并跟踪自己的表现。本研究将自身置于城市新陈代谢和复杂性理论的联系之中。城市新陈代谢是对城市中物质和能量流动的研究。本研究的主要假设是，城市新陈代谢在社区尺度上遵循不同的数学规律，可以使用复杂性理论的元素来捕获。各种数学定律（例如，幂律，对数正态分布，均匀分布）将应用基于代理的建模技术来生成一个理论空间，该空间将包括能源和资源消耗方面的每个可能的社区概况。然后，这些法律将使用市政开放数据门户网站免费提供的数据，在各个社区进行测试。此外，本研究将利用机器学习的元素，根据社区的能源和资源消耗模式对社区进行分类，并利用网络科学，更好地了解电力、水、天然气和交通基础设施的使用之间的相互依存关系。因此，这项研究将为帮助设计一个更智能、更有弹性，同时通过需要更少的能源和资源来实现更可持续发展的社会做出重大贡献。

Ultimately, the amount of energy and resources that Americans consume depends heavily on where as well as how they live. Thus residents of northern states tend to consume more energy at home than residents of southern states, because heating requires more energy than cooling. Similarly, people living in suburbs tend to consume more energy for transportation than people living in city centers because longer commutes consume more energy. Similar observations can be made for all types of energy and resources, from electricity and transportation to natural gas, water, and even food. This Faculty Early Career Development (CAREER) grant will help to discover the fundamental principles that govern how much "where" and "how" people live matters for energy and resource consumption. The contributions from this research will directly assist in the development of effective policies for more sustainable communities that consume less energy and resources. Moreover, a better understanding of energy flows in cities will provide planners and engineers with information that will enable them to design smarter and more resilient infrastructure systems that are decentralized and distributed. In addition, this grant will have a significant impact for the broader public as it includes the development of a smartphone application to enable anyone to calculate their daily carbon footprint and track their performance over time.This research places itself at the nexus of urban metabolism and complexity theory. Urban metabolism is the study of flows of material and energy in cities. The main hypothesis of this research is that urban metabolism follows distinct mathematical laws at the community scale that can be captured using elements of complexity theory. Various mathematical laws (e.g., power law, lognormal distribution, uniform distribution) will be applied using agent-based modeling techniques to generate a theoretical space that will include every possible community profile in terms of energy and resource consumption. These laws will then be tested for individual communities using freely available data from municipal open data portals. Additionally, this research will utilize elements of machine learning to classify communities based on their energy and resource consumption patterns, and network science to better understand the inter- and co-dependence between the usage of electricity, water, natural gas, and transportation infrastructure. The research will therefore offer a significant contribution to help design a society that is smarter and more resilient while being more sustainable by requiring less energy and fewer resources to thrive.