NSF2026: EAGER: Spatio-Temporal Design of Techno-Ecological Synergies for a World without Waste and Resilient Landscapes

NSF2026：EAGER：技术生态协同效应的时空设计，打造一个没有废物和有弹性景观的世界

• 批准号: 2036982
• 负责人: Bhavik Bakshi
• 金额: $ 29.99万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036982&HistoricalAwards=false
• 关键词: NSF2026; EAGER; Spatio; Temporal; Design; NSF2026; EAGER; Spatio; Temporal; Design

With support from the CBET/ENG Environmental Sustainability program and the NSF 2026 Fund Program in the Office of Integrated Activities, the investigators are researching the ecological capacity to provide goods and services in the face of demands imposed by a technological society. To meet sustainability goals, most engineers design and operate manufacturing processes to minimize resource use and emissions, but they may not account, for example, for the capacity of a watershed to provide fresh water to all users (including non-human users) or of the atmosphere to absorb emitted CO2. Similarly, economists may exclude consideration of the impact on ecosystems. The vision of this research is that through appropriate design, human activities can explicitly account for the provisions supplied by ecosystems, and can be designed to respect ecosystem limits. The research seeks to provide a framework for designing industries and ecosystems simultaneously to operate in a mutually beneficial or synergistic manner. The resulting Techno-Ecological Synergies (TES) will rely on designing ecosystems of the future, that in fundamental concept include the built environment, to enrich the NSF2026 Idea Machine winning entries of a "World without Waste," and "Large Landscape Resilience by Design."TES design will be formulated as an optimization problem. Novel and innovative approaches for developing designs at relevant spatial and temporal scales are proposed for solving the optimization problem. One such innovation to be realized is the the development of physics-based surrogate models with deep neural networks to capture the spatio-temporal variation of pollutant concentration in a selected region for a point source under various geographical, land cover, and meteorological conditions, embracing uncertainty issues. Advanced and innovative stochastic and/or dynamic programming methods will be employed to obtain TES designs. As a test case, the developed TES approach will be applied to a power plant near Cincinnati and vegetation on the surrounding landscape. For this case study, conventional and TES designs will be compared in terms of their contribution to reducing waste at the landscape and life cycle scales. To assess large landscape resilience for conventional and TES designs, future climate change scenarios will be simulated and landscape resilience compared for conventional and TES designs in terms of regional water availability and air quality. This approach will assess the benefits of seeking synergies with nature through the TES framework. The results of this work are targeted to lay the foundation for further work toward the convergence of disciplines including ecology, sociology, economics, public policy, statistics, environmental science, and engineering. The aspiration of the TES approach is to see, with time, practical implementation of TES on the scale of industrial, urban, and agro-ecological landscape networks.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.

在CBET/ENG环境可持续性计划和NSF 2026基金计划的支持下，研究人员正在研究面对技术社会的需求，以提供商品和服务的生态能力。为了满足可持续性目标，大多数工程师设计和运营制造过程以最大程度地减少资源的使用和排放，但可能不会考虑到流域为所有用户提供淡水（包括非人类用户）或气氛吸收发射二氧化碳的淡水的能力。同样，经济学家可以排除对生态系统影响的考虑。这项研究的愿景是，通过适当的设计，人类活动可以明确说明生态系统提供的规定，并且可以设计以尊重生态系统限制。该研究旨在为设计行业和生态系统同时以互惠互利或协同的方式设计框架。由此产生的技术生态协同效应（TES）将依靠设计未来的生态系统，在基本概念中包括建筑环境，以丰富NSF2026 Idea Indure Machine在“无浪费”和“大型景观弹性的弹性”条目中赢得设计。提出了在相关的空间和时间尺度上开发设计的新颖和创新方法，以解决优化问题。要实现的创新是一种具有深层神经网络的基于物理基于物理的替代模型的发展，以捕获所选区域中污染物浓度的时空变化，用于在各种地理位置，土地覆盖和气象条件下的点源，以接受不确定性问题。将采用高级和创新的随机和/或动态编程方法来获得TES设计。作为测试案例，开发的TES方法将应用于辛辛那提附近的发电厂和周围景观上的植被。对于此案例研究，将根据景观和生命周期量表减少废物的贡献来比较常规设计和TES设计。为了评估传统和TES设计的巨大景观弹性，将模拟未来的气候变化情景，并在区域水的可用性和空气质量方面对传统和TES设计进行景观弹性。这种方法将通过TES框架评估寻求与自然协同作用的好处。这项工作的结果旨在为进一步的努力奠定基础，以促进学科的融合，包括生态学，社会学，经济学，公共政策，统计，环境科学和工程。 TES方法的愿望是，随着时间的流逝，TES的实际实施是在工业，城市和农业生态景观网络的规模上进行的。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准来评估的值得支持的。